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Structural analysis based sensor measurement fault diagnosis in cement industries
Control Engineering Practice 64 (2017) 148–159
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Control Engineering Practice journal homepage: www.elsevier.com/locate/conengprac
Structural analysis based sensor measurement fault diagnosis in cement industries V. Gomathi b, Seshadhri Srinivasan a,n, K. Ramkumar b, Guruprasath Muralidharan c a
Berkeley Education Alliance for Research in Singapore, Singapore 138602, Singapore School of Electrical and Electronics Engineering, SASTRA University, India c Smarta Opti Solutions, Chennai, India b
art ic l e i nf o
a b s t r a c t
Article history: Received 4 August 2016 Received in revised form 23 February 2017 Accepted 25 February 2017
This investigation presents a fault diagnosis methodology for detecting sensor faults in cement industries pyro processing section. It works in three steps: (a) modelling, (b) analysis, and (c) validation. In the modelling, the actual data from the cement pyro processing is used to do a correlation analysis between output and input variables. The structural model is obtained from the correlation tests. During the analysis phase the Structural analysis Tool (SaTool) is used to detect the detectability and isolability of the faults. The results of the structural analysis are validated in a cement industry using residual analysis performed using structural sensor model and real-time measurements. The main advantages of this fault diagnosis technique are: (a) it requires only correlation analysis to obtain the structural model without a detailed physical model as in other methods, (b) conclusions regarding detectability and isolability can be easily drawn during the analysis stage itself, and (c) the method is simple compared to the model-based, and data-history based methods. The effectiveness of the proposed method is illustrated using data from cement pyro processing plant and its performance is compared with model based approaches for four different types of sensor faults: (1) bias, (2) drift, (3) stuck, and (4) measurement failures. Our results demonstrate that the structural method is able to detect the sensor faults even in the presence of noisy information, and its performance is comparable with that of model based approaches without employing a physical model. & 2017 Published by Elsevier Ltd.
Keywords: Fault diagnosis Structural model Measurement faults Cement kiln Structural analysis SaTool
1. Introduction Sensor measurement faults are frequent in pyro processing section of cement industries due to hazardous operating conditions, improper purging and location of sensors, contamination, and material deposition. Early detection of measurement faults in pyro processing improves production and product quality in cement industries. Furthermore, they are important for avoiding abnormal and unsafe operating conditions. As detecting measurement faults requires continuous monitoring, complete automation of the task is necessitated and fault diagnosis systems are used to this extent. Traditionally, in cement industries fault diagnosis systems are integrated with advanced control systems for building fault tolerant control. Realizing this importance, scientists and researchers n
Corresponding author. E-mail addresses: [email protected] (V. Gomathi), [email protected] (S. Srinivasan), [email protected] (K. Ramkumar), [email protected] (G. Muralidharan). http://dx.doi.org/10.1016/j.conengprac.2017.02.012 0967-0661/& 2017 Published by Elsevier Ltd.
have devoted significant attention in designing fault diagnosis schemes in cement industries. The available approaches can be broadly classified into four categories, they are: (i) quantitative model based methods (Gao, Cecati, & Ding, 2015a; Venkatasubramanian, Rengaswamy, & Yin, 2003), (ii) process history based approaches (Venkatasubramanian, Rengaswamy, Kavuri, & Yin, 2003), (iii) qualitative methods (Venkatasubramanian, Rengaswamy, Kavuri, 2003), and (iv) hybrid approaches (Gao, Cecati, & Ding, 2015b). Quantitative model based require complex mathematical description of dynamic process that are difficult to obtain in practice (Sadeghian & Fatehi, 2011). This makes their adaptation in cement industries difficult. Process history based approaches use past information collected from the plant and soft-computing tools such as artificial neural networks, fuzzy or hybridization of these techniques to build a fault detection scheme (Makaremi, Fatehi, Araabi, Azizi, & Cheloeian, 2009; Mahdaoui, Mouss, Mouss, & Chouhal, 2011). The investigation in Makaremi et al. (2009) studied the detection of super chilling and super-heating of kiln using locally linear neuro fuzzy model. The investigation used motor power consumption to
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Fig. 1. Process flow in cement plant (F. A. Systems, 2015).
monitor the abnormal conditions inside the kiln. A fuzzy inference based fault diagnosis method for detecting faults was proposed in Correcher, Morant, Garcia, Blasco-Gimenez, and Quiles (2002). More recently, Talebnezhad, Fatehi, and Shoorehdeli (2013) used fuzzy clustering algorithm for fault detection and isolation in cement kilns. The investigation used signal-based methods and three clustering algorithms to design fault diagnosis technique. The role of neuro fuzzy approaches for detecting actuator faults have been studied in Mahdaoui et al. (2011). Further, the investigation studied the generalization and reasoning capability. In process history based methods, selection of the classification and clustering tool, generalization to various faults, and the presence of only finite sampling of the distribution of the class of measurement data space are major drawbacks that make adaptation in industries difficult. Hybrid approach that combined support vector machines (SVM) and binary ant colony optimization for rotary cement kiln has been studied in Kadri, Mouss, and Mouss (2012). Still, the selection of SVM as a classifier due to its accuracy cannot be generalized for a wide class of sensor faults. More recently, structural approach for detecting faults in cement kilns was studied in Daniali (2013). The structural approach uses an abstract model leading to significant simplicity of the fault diagnosis scheme. Furthermore, important properties of the fault diagnosis such as detectability and isolability can be inferred from structural analysis. Structural approach has been used for various applications such as refrigeration systems (Hovgaard, 2009), wind turbines (Blesa, Puig, Romera, & Saludes, 2011; Blesa, Rotondo, Puig, & Nejjari, 2014), electrical distribution systems (Knüppel, Blanke, & Østergaard, 2014), vehicle navigation (Falkenberg, Gregersen, & Blanke, 2014), unmanned air-craft systems (Sørensen, Blanke, & Johansen, 2015), lithium-ion batteries (Liu, Ahmed, Zhang, Rizzoni, & He, 2016), ship propulsion system (Izadi-Zamanabadi & Blanke, 1999) and chemical systems (Zhang, 2010). To our best knowledge, the role of structural analysis has not been extensively studied in pyro processing. Motivated by this research gap, this investigation aims to develop a fault diagnosis scheme for pyro processing by combining structural analysis, process simulations, and field experiments. Departing from traditional quantitative approaches, correlation analysis on input–output data from either real-experiment or
process simulations that mimic actual conditions can be used to build structural model for analyzing sensor faults. Then residual between the sensor structural model and actual plant data from a cement pyro processing is used to validate the structural analysis. The validated sensor models are then used for building reliable fault-tolerant controllers for pyro processing. The advantages of the method are: need for only correlation data as against detailed physical models in quantitative approaches, ability to provide insights on desirable features such as detectability and isolability during design stage, and the simplicity of the fault diagnosis scheme. Main contributions of the investigation are: (i) a new fault diagnosis design technique that combines process/simulation data, structural model, and experimental measurements, (ii) a workflow for designing reliable fault diagnosis scheme using structural analysis for cement pyro processing, and (iii) validation of the proposed scheme in a cement industry. The paper is organized into five sections. Section 2 describes the cement manufacturing and the cement kiln process. In addition the motivation for building the fault diagnosis scheme based on structural model is also discussed. The proposed approach and the work flow for building fault diagnosis schemes for cement kilns is presented in Section 3. Validation of the fault diagnosis scheme using industrial measurements is illustrated in Section 4. Conclusions and future prospects of this investigation are discussed in Section 5.
2. Problem formulation The cement manufacturing consists of three stages: raw mix preparation, pyro processing, and grinding as shown in Fig. 1. The raw materials quarried from the mines are crushed and blended with additives in a predefined proportion to be stored in silos. In pyro processing, the raw material is converted to clinkers at around 100 °C in three steps: pre-heating, calcination and burning as shown in Fig. 2. During pre-heating 70% of pre-calcination is performed at a temperature of 900 °C, followed by de-carbonation in calciner. The calcined feed (1000 °C) is given to higher end of the rotary kiln and fuel is fed at the lower end. The material in the
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Fig. 2. Schematic of clinker process (F. A. Systems, 2015).
kiln is burned to very high temperature and the inclination of the kiln is used to move the material from higher to lower end at a temperature of around 1450 °C. The output thus obtained is passed on to grate cooler, where the clinker is formed at a temperature of around 100 °C. The last stage is the cement grinding circuit that takes the clinkers and produces powdered cement in correct ratio. The pyro processing stage uses various sensors such as oxygen analyzers, temperature measuring devices in the cyclones and burning zone area that are subjected to frequent faults and may completely fail due to the environment. The faults can be broadly classified as: (1) Process faults which may occur due to the problem with mechanical components or other operating changes. (2) Sensor/measurement faults due to failures in sensors. Sensor fault/failure may occur as they are placed in a dusty environment and sometimes subject to manual interventions. Such failures also influence the product quality, and throughput of the cement industry. Therefore, early detection of such faults is important for operating the cement industry in a reliable and efficient manner. As pyro processing is a complex process and building mathematical models describing their behavior is cumbersome and it limits the possibility of using quantitative approaches for faultdiagnosis. History based approaches require data from different sensors for various faulty conditions. Furthermore, their generalization to deal with different sensor faults is rather difficult. In this backdrop, qualitative approaches based on structural models provide a simpler way to build fault-diagnosis schemes. One of the shortcomings of the qualitative approach is the generation of spurious signals (Venkatasubramanian, Rengaswamy, Yin, et al., 2003) and it can be eliminated by validating the qualitative approach with sensor measurements obtained from cement kiln. Moreover, structural analysis reveals important features (e.g. detectability) of the fault-diagnosis scheme during design stage itself, whereas other methods require residual analysis to draw conclusions on these features. This investigation first uses simulation on simple process models to obtain data useful for studying correlation between sensor measurements and system variables. The correlation analysis results are then used to choose the process variables to obtain structural model that is followed by structural analysis to identify desirable properties of fault diagnosis schemes during the analysis stage. The SaTool (Structural analysis Tool) a Matlab based package developed by Blanke and Lorentzen (2006) and Willersrud, Blanke, and Imsland (2015) is used to perform structural analysis. To
validate the results, residual analysis between structural sensor model and real-time measurement data from the cement industry obtained using FLSmidth Pvt. Ltd., supervisory control and data acquisition (SCADA) systems is used.
3. Measurement fault diagnosis in cement industries 3.1. Work-flow for building FDI The structural analysis scheme has three phases: Modelling, Analysis, and Validation as shown in Fig 3. The various stages are detailed below. 3.2. Modelling 3.2.1. Data collection The first step in building the fault diagnosis scheme is performing experiments/simulations to obtain sensor data and process variables using ECS/CEMulator1 which is a software that mimics the actual plant operations. A screen shot of the cement process is shown in Fig. 4. The structural model is obtained using process knowledge gathered from simulations performed on CEMulator and correlation tests on sensor data with process variables. The input variables of the simulator are kiln feed rate U1, kiln fuel rate U2, and kiln speed U3, whereas the sensor measurements pre-heater inlet oxygen Y1, kiln inlet temperature Y2 and burning zone temperature Y3, respectively. Variables U and Y denote the input and output variables of the cement kiln system. Variations of the kiln speed, kiln fuel rate, kiln feed rate and sensor measurements are recorded from the simulator as shown in Figs. 5–7, respectively. These variables are selected using studies on ECS/CEMulator and recording the sensor data. The data can also be used to obtain a transfer function model (Sivanandam, Kannan, Srinivasan, & Muralidharan, 2016; Venkatesh, Ramkumar, Guruprasath, Srinivasan, & Balas, 2016). The transfer function model obtained from the data is provided in Appendix A. Clearly, the model can be used for repeating the approach described in the paper. 1
ECS is a proprietary simulator of FLSmidth Pvt. Ltd.
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Fig. 3. Work-flow for building FDI in cement kilns.
3.2.2. Correlation analysis In the second step, the recorded data (sensor measurements w. r.t. inputs) from the CEMulator is subjected to a correlation analysis, wherein Pearson's correlation coefficient denoted by
−1 ≤ r ≤ 1 quantitatively describes the dependencies of the sensor measurement on system variables. As sensor measurements are recorded based on cement industry ambiance, noise and disturbances are reflected by the CEMulator and are included
Fig. 4. Screen shot of the pyro processing in ECS/CEMulator.
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3.4 3.3
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Pre-heater inlet oxygen(%)
Burning zone o temperature( C)
Kiln inlet o temperature( C)
Kiln fuelrate(tph)
inherently in correlation analysis. The correlation coefficient recorded for the inputs and outputs considered is shown in Table 1 and the operating points used for experiments in ECS/CEMulator are shown in Table 2. Results of the correlation test are used to choose the process variables to build the structural model, wherein the constraints define the relation between the system variables with sensor measurements. The structural model is a functional abstracted model that eliminates the need to know the complex process dynamics. It uses correlation (functional relation) between sensor measurements and system variables for capturing the dynamics required for fault diagnosis. The structural analysis uses the structural model of a system, represented in a bipartite graph showing the links between variables, parameters and constraints
(Hovgaard, 2009). It simplifies the complex dynamical system to a low level simple model of the system behavior (Blanke, Kinnaert, Lunze, Staroswiecki, & Schröder, 2006; Blanke, Staroswiecki, & Wu 2001). The analysis reveals useful information on which components of the system are subjected to faults and can be easily detectable or isolable. 3.2.3. Structural model The third step defines the structural model, wherein the constraints C that qualitatively relate the known and unknown variables are formulated. They define the correlations between sensor measurement and system variables. Let us define:
Z=κ∪χ
(1)
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Kiln inlet o temperature( C) Kilnfeedrate(tph)
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No of Samples Fig. 7. Variations in measurements for change in kiln feed rate.
Table 1 Correlation between input and output variables.
Table 3 Notation.
Pre-heater Kiln inlet Burning zone inlet oxygen (%) temperature (°C) temperature (°C) Feed rate (tonnes/ hour) Fuel rate (tonnes/hour) Speed in rpm
0.7520631
0.8463395
0.6786097
0.90146 0.75448806
0.913 0.917706251
0.53072 0.822512698
Symbol Definition
Table 2 Operating points: inputs.
400 (tonnes/hour) 9.7 (tonnes/hour) 3.2 (rpm)
where Z is the set of all known and unknown variables, κ the set of known variables and χ the set of unknown variables. Here the set of known variables are κ = {U1, U2, U3, Y1, Y2, Y3} and unknown variables, χ = {ZFE , ZFU , ZO2, ZKIT , ZBZT , ZSP }. The constraints are given as:
C1:
ΔU1 ≡ ZFE
C2:
ΔU2 ≡ ZFU
C3:
ΔU3 ≡ ZSP
C4 :
ΔY1 ≡ ZO 2
C5:
ΔY2 ≡ ZKIT
C6:
ΔY3 ≡ ZBZT
C7:
ZO 2 ≡ G11ZFE + G12ZFU + G13ZSP
C8:
ZKIT ≡ G21ZFE + G22ZFU + G23ZSP
C9:
ZBZT ≡ G31ZFE + G32ZFU + G33ZSP
Kiln speed (in rpm) Kiln feed rate (tonnes/ hour) Kiln fuel rate (tonnes/hour) Pre-heater inlet oxygen %
ZKIT ZBZT
Kiln inlet temperature in °C Kiln burning zone temperature °C
Disturbance terms are added in measurements
Input variable Operating point Feed rate Fuel rate Speed
ZSP ZFE ZFU Z O2
represents the relative gain which denotes the change in output to that of the change in input, U1, U2, U3, Y1, Y2, Y3 are the control inputs and outputs, Δ represents the rate-of-change of the variable, respectively. Constraints 1–3 give the relation between control inputs U and physical inputs Z i.e., ZFE , ZFU , ZSP . While constraints 4–6 relates the controlled variables (Y) and physical outputs ZO2, ZKIT , ZBZT , respectively. The sensor models of oxygen analyzer (ZO2), thermocouple (ZKIT ), and pyrometer (ZBZT ) are modelled using constraints 7, 8 and 9, respectively. Constraint 7 gives oxygen analyzer model which maps ZO2 to ZFE , ZFU , ZSP with the relative gain of G11, G12, G13. Constraint 8 gives thermocouple sensor model which maps ZKIT to ZFE , ZFU , ZSP with the relative gain G21, G22, G23. Constraint 9 gives pyrometer sensor model that maps ZBZT to ZFE , ZFU and ZSP with the relative gains G31, G32, G33. 3.3. Analysis
(2)
where Z represents the physical variable shown in Table 3 and G
3.3.1. Incidence matrix The incidence matrix represents the relationship between the constraints and variables, which are arranged in rows and columns as shown in Table 4. The numeric 1 represents the link between the constraints and variables. The variables U1–Y3 are known, whereas the last 6 columns in the incidence matrix represent the unknown variables.
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Table 4 Incidence matrix.
C1 C2 C3 C4 C5 C6 C7 C8 C9
Table 7 Dependency matrix for Matching 1.
ΔU1
ΔU2
ΔU3
ΔY1
ΔY2
ΔY3
ZFE
ZFU
ZSP
Z O2
ZKIT
ZBZT
1 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0
0 0 0 0 0 1 0 0 0
1 0 0 0 0 0 1 1 1
0 1 0 0 0 0 1 1 1
0 0 1 0 0 0 1 1 1
0 0 0 1 0 0 1 0 0
0 0 0 0 1 0 0 1 0
0 0 0 0 0 1 0 0 1
r1 r2 r3
C4
1 1 1
1 1 1
1 1 1
1
C5
C6
C7
C8
C9
1 1
1 1
1
C7(C1(U1), C2(U2), C3(U3), C4(Y1)) = 0
ZFU
ZSP
Z O2
ZKIT
ZBZT
1 0 0 0 0 0 1 1 1
0 1 0 0 0 0 1 1 1
0 0 1 0 0 0 1 1 1
0 0 0 1 0 0 0 0 1
0 0 0 0 1 0 0 1 0
0 0 0 0 0 1 1 0 0
3.3.2. Canonical decomposition Using canonical decomposition method the constraints and variables are rearranged in order to divide the system into three – over constrained, just constrained and under constrained system as shown in Table 5. Redundancy will exist only in over constrained systems. Over constrained system are denoted by
(3)
3.3.3. Complete matching Matching is the procedure of assigning every unknown variable (χ) to each of the constraints. A matching is said to be Complete, if there exists an onto mapping between unknown variables χ and set of all constraints. It is Incomplete if there are unmatched constraints and they are used for deriving the analytical redundancy relation (ARR) which denotes the time evolution of known variables (κ) when the system operates in normal condition. It gives the relation among the constraints and residuals that are generated using backtracking method. The fault diagnosis technique checks whether the ARR is satisfied during each time epoch. In the event of a fault κ fails to satisfy the ARR that meets the following important properties: 1. It should not respond to any unknown inputs and parameters. 2. It should be sensitive to the faults. For the process under study, the Structural analysis Tool (SaTool) was used to perform complete matching for 86 different combinations. We have considered one of the matching in our analysis shown in Table 6. From the table it is observed that the 6 unknown variables were matched with constraints C1 to C6, whereas the constraints C7 to C9 are used to compute the residuals:
(4)
3.3.4. Detectability and isolability A close inspection of the constraints in (2) reveals that the constraints C7, C8 and C9 depend on the unknown variables χ. The first six constraint equations C1–C6 with known variables κ are used to replace the right hand side of the constraint equations in the unmatched constraints C7 to C9. This procedure is repeated until all unmatched constraints are replaced with known variables. This gives the ARR or parity equations (4). The parity equation can be used to draw useful insights into two important features of the fault-diagnosis scheme namely detectability and isolability. Detectability is the ability of the fault diagnosis scheme to identify a particular fault, whereas isolability denotes the capability to identify the component that caused the fault. The dependencies of the constraints on the residuals of the parity equations is modelled using dependency matrix. The columns represent the constraints while the rows capture the residuals. An entry of 1 in a column denotes the effect of the residual on the corresponding constraint. The dependency matrix obtained using parity equations is shown in Table 7. Lemma 1. Blanke et al., 2006). A violation of a constraint C is structurally detectable iff, it has a nonzero Boolean signature in some residual r
C ∈ Cdetectable ⇔ ∃ r: C ≠ 0 ⇒ r ≠ 0
(5)
Lemma 2. Blanke et al., 2006). A violation of a constraint Ci is structurally isolable if and only if, it has a unique signature in the residual vector, i.e. column mi of M is independent of all other columns in M:
C ∈ Cisolable ⇔ ∀ j ≠ i: mi ≠ mj
(6)
The detectability of the fault can be inferred from Lemma 1. In principle it means that, if the residuals r1 to r3 have a Boolean signature for at least one of the residuals then the fault is detectable. From the dependency matrix in Table 7, each constraint has at least one residual having Boolean signature 1 in one of the constraints. Therefore, we can infer that the constraints are detectable from the residuals. These constraint equations are used to build the sensor models (described later) and it captures the rateof-change of residual for a given change in input. Thus, if the constraints can be detected, then the faults involving these corresponding constraints can also be detected from the sensor models. Similarly, the faults are isolable if the columns of dependency matrix are independent. One can observe that the Table 8 Structural property matrix for Matching 1.
Table 6 Matching 1. Constraints Variables
C3
C8(C1(U1), C2(U2), C3(U3), C5(Y2)) = 0
ZFE
S + = |Z +| < |C +|
C2
C9(C1(U1), C2(U2), C3(U3), C6(Y3)) = 0
Table 5 Canonical decomposition.
C1 C2 C3 C4 C5 C6 C9 C8 C7
C1
C1 ZFE
C2 ZFU
C3 ZSP
C4 Z O2
C5 ZKIT
C6 ZBZT
C7 0
C8 0
C9 0
Property Detectability Isolability
C1 D N
C2 D N
C3 D N
C4 D N
C5 D N
C6 D N
C7 D N
C8 D N
C9 D N
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sensor faults are detectable, but as there are dependent columns in the dependency matrix they are not isolable (Table 8). 3.4. Validation In the validation stage, the structural sensor model is compared with actual sensor measurements to generate residuals. 3.4.1. Sensor model To validate the structural approach actual sensor measurements are equated with the structural model of the sensor. The sensor models are obtained from constraints C7, C8, C9 . The sensor models proposed will be used in structural analysis for detecting faults. As will be seen later, quantitative methods will require exact mathematical description for modelling the system. 3.4.2. Residual analysis In the last step, the residuals that are difference between the output of the structural sensor model and the actual measurements are studied. The deviation of the residuals indicates that there are faults. We used a threshold for the bias fault <4% of the nominal value of the sensor measurement. This means if the deviation is ≥4% then a bias fault is concluded. However, our experiments revealed that such thresholds are not required for other faults. This deviation is only due to sensor fault and not due to the set-point changes given by the operator as the residual signal encapsulates the set-point change in both the sensor measurements and structural model.
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structural method uses just the correlation information between sensor data to obtain the structural model. Though both these methods use residual analysis for fault detection, the structural method uses the rate-of-change of the residual as against the actual residual used in quantitative techniques. This provides significant simplicity as it reflects directly the occurrence of the fault i.e. there is significant deviation in output for a small or no change in input. To study the robustness and performance of the methods to different sensor faults considered in our analysis, the experiments were repeated. During our experiments the noise levels were added to the data purposefully to study the robustness and the findings are reported in the subsequent parts of this section. 4.3. Methodology The methodology used for our experiments has four steps:
This section illustrates the structural method applied to fault diagnosis in pyro processing based on experimental data obtained from cement industry and compares it with model based approach. A comparison of fault-diagnosis capability, engineering simplicity, and robustness is taken up for analysis.
1. Plant data collected from CEMulator was used to build the qualitative model using correlation analysis as described in Section 3.1. 2. In the second step, the structural model is obtained by performing correlation analysis between the input and output variables. Initially, the correlation test selects the variables for constructing the structural model and highly correlated variables are chosen for the analysis. In our case, all the three input variables have a high correlation with the output variables. So the three input–output variables are used for building the sensor model for the structural method. The relative gains Gij maps the changes in the input variables to the output of the sensor model. With the abuse of terminology used in structural analysis, this investigation uses the structural model to denote the sensor model in the presentation of the analysis. 3. The rate of change of residual is used for structural analysis, whereas the residual is computed for quantitative method. 4. Conclusions are drawn on the faults based on the residual analysis.
4.1. Sensors and fault classification
4.4. Quantitative methods
Choice of the sensor, and the type of fault is important for validating the fault-diagnosis scheme. This investigation chooses kiln inlet temperature measured by a thermocouple, burning zone temperature obtained from a pyrometer, and pre-heater inlet oxygen measured using an analyzer as the sensors. The choice is dictated by the influence of these sensors on cement production and product quality as closed loop controllers are mainly dependent on them. The following sensor faults are considered in our analysis:
To evaluate the performance of quantitative methods, the residuals between the transfer function model and the plant data for the four faults (bias, drift, stuck and measurement failure) are considered. We present the analysis of different faults by compressing the time for non-faulty periods. The time instants mentioned below are scaled for clarity of presentation and do not represent actual time-instants. However, they are indicative of the faults in terms of amplitude and the ratio of normal working to faulty condition. Fig. 8(a) shows the residual signal of the kiln inlet temperature subjected to bias (between 100 and 150 samples), and drift (from 400 to 500 samples). During 100 to 150th sample, the residual signals show a constant amplitude of 5% which indicates a bias fault. Similarly, the residual signal shows a gradual deviation from zero in the 400–500 samples. This denotes the existence of a drift fault. The fault diagnosis system raises an indication about the faults to the advanced process control layer for taking corrective actions. Similarly, the pyrometer residual obtained using quantitative model for different faulty conditions are studied. Fig. 8(b) shows the performance of the method for bias fault (from 300 to 350 samples), and sensor stuck (from 500 to 550 samples). A residual signal of 5% constant amplitude represents a bias fault, whilst a very small deviation from the actual value but not changing with time indicates that it is a sensor stuck. The quantitative method was also used to study the oxygen
4. Results
1. Bias fault wherein a constant amplitude signal is added to the measured value due to deposition of dust or a coating on the sensor. 2. Drifts are the deviations of the measurements from the actual value due to ageing, noise and other factors. 3. Sensor stuck occurs due to manual or signal interruption and the sensor measurement is stuck to its previous value. 4. Sensor measurement failure is the absence of measurements due to failure of connecting probes or wire breakage. 4.2. Description of the experiment To illustrate the advantages of structural method, two sets of tests are reported: (i) quantitative model based approach and (ii) qualitative structural approach. While the quantitative method relies on the physical model (e.g. transfer function model), the
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analyzer. The residual signals obtained for bias (between 200 and 250th sample and from 500 to 550 sample) and sensor measurement failure (between 600 and 650th sample) are reported in Fig. 8(c). One can verify that the residual signal with constant amplitude of 5% and 10% is noticed during the different time-instants. This indicates the presence of the bias fault. Similarly, change in residual from zero to 100% between time-instants 600– 650 sample) means there is a 100% fault in measurements and it can be concluded that it indicates a complete sensor measurement failure. 4.4.1. Robustness analysis To study the robustness of the sensor faults to noise, residual
signals under noisy conditions were analyzed. It was found that the oxygen analyzer is not subjected to noise and therefore, was not considered in the analysis. The residuals of the kiln inlet temperature (thermocouple) and burning zone temperature (pyrometer) subjected to bias faults with noise are shown in Fig. 9 (a) and (b), respectively. It can be seen that the thermocouple residual signal deviates from zero at the 100th sample and gradually increases till 200th sample. This indicates a sensor drift and a deviation in the sensor signal between 400 and 450th samples with constant amplitude 5% of nominal value indicates the bias fault. Although, subjected to noise the quantitative method was able to identify the faults clearly. However, an analysis of pyrometer residual signals revealed that bias fault could be identified
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using the residual analysis (between 300 and 350 sample), the sensor stuck fault was detected as a change in sensor signal (from 400 to 500th sample). The result demonstrates that quantitative methods have significant robustness in identifying bias, drift, stuck, and sensor measurement faults.
shown in Fig. 11. The residual signal changes at the 100th and 130th sample with a constant amplitude of 74% and there is a change in pyrometer residual signals at 400th and 500th sample with a constant magnitude of 75% indicating the presence of a bias fault during the period. The residual signal is not affected by noise since the fault magnitude is higher than that of the noise.
4.5. Structural method
4.7. Multiple faults with noise
In qualitative methods, the rate of change of residual between the structural model and actual data is used for detecting the fault. Here the use of structural method for bias, drift, and sensor measurement failure is presented. Fig. 10(a) shows the rate of change of residual of kiln inlet temperature, from the residual signal one can verify that there is a change in its residual signal with 5% amplitude at the 94th sample and 5% amplitude at 123 sample. These changes in sensor measurement with constant amplitude indicate the presence of a bias fault during those time instants. In addition, the signal also shows that there is a minimum deviation in the residual from zero at the 195th sample till 215th sample. This indicates the presence of drift in the sensor measurement. The use of structural method for studying faults in pyrometer measuring zone temperature is reported in Fig. 10(b). A close inspection reveals that there is a change of residual from 0 to þ100% at the 394th sample, and 100% change in amplitude at 413th sample. These changes reveal the presence of complete sensor measurement failure during such time instant. The signal also shows small deviation during 595 to 625th samples which indicates a drift in sensor measurement. The residual signal of the oxygen analyzer for bias fault is reported in Fig. 10(c). The amplitude of the residual signal changes from 390 to 440 samples, and 550–580 samples from zero to þ10% and 10% and þ4% and 4%, respectively. This indicates the presence of bias fault in the sensor.
Fig. 12 (a) shows the change in residual signal of kiln inlet temperature and burning zone temperature under noisy condition for different faults. There is a change in the residual signal at 100th and 130th sample with the constant amplitude of 74%, so it is observed that there is a bias fault that occurs in thermocouple measurement. The change in residuals also shows deviations of around 2.5% approximately at 200 sample thus indicating the presence of sensor drift. The residual signal of pyrometer sensor showing changes in the residual with 7100% change in amplitude at 390th and 415th sample means that the sensor measurement failure during that time. This signal also shows that there is deviations in the residual with 75% amplitude change during 300 to 350th and 600 to 700th samples. This deviation is higher than the noise magnitude, and it is concluded that bias faults have occurred. The noise signals are not visible in the graph as their magnitude is lesser than residuals. To conclude, both quantitative and qualitative methods were applied to detect faults in cement industry. Our analysis found that while the performance of both these methods were comparable, the qualitative method provided significant simplicity and advantages that is not possible with the quantitative approach. This is mainly because, the qualitative methods used only the correlation analysis as against the accurate physical model required for quantitative method. Furthermore, the structural analysis answers questions regarding the isolability and detectability during the design stage, whereas an actual residual calculation is required to determine these features in quantitative methods. Moreover, as the structural method used rate of change of residual provides simplicity as it directly relates to the occurrence of the fault i.e. an abnormal change in the output for a small/no change in the input. Therefore, qualitative methods provide significant advantages in
4.6. Robustness of structural method The residual signal of thermocouple and pyrometer under noisy condition for the bias fault with noisy measurement is 5
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building fault diagnosis methods in cement industry.
5. Conclusion This paper presented a fault diagnosis (FDI) technique based on structural method to identify sensor faults in cement industry. The proposed approach combined simulations (in CEMulator), structural model, structural analysis, and experimental data from cement kilns to build and validate the structural FDI scheme. First, experiments were performed in ECS/CEMulator software from FLSmidth Pvt. Ltd. The experimental data was used to build both structural and transfer function model. Correlation analysis and system identification techniques were used to build the structural and transfer function model, respectively. The use of correlation analysis as
against high fidelity physical models provided significant simplicity to the structural method. The structural analysis tool (SaTool) was then used to analyze the structural model to answer questions such as isolability and detectability of the sensor faults. These features can be verified for quantitative methods only after residual analysis. Thus, significant insights into the FDI capability can be ascertained during the design stage itself with the structural analysis. The investigation provided a work-flow that can be used to build a FDI and it consisted of three stages: (1) Modelling, (2) Analysis, and (3) Validation. In the modelling phase the structural model was generated from correlation analysis. The analysis phase used SaTool to answer questions regarding the features of FDI. Finally, in the validation phase the results were validated with actual sensor measurements. To illustrate the advantages of the structural approach the
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method was compared with quantitative model based approach. The performance of these methods to faults in kiln temperature, burning zone temperature, and oxygen analyzer were studied. The bias, drift, sensor stuck, and sensor measurement failure were analyzed. Our results showed that structural analysis were not able to identify sensor stuck fault. However, the other faults were identified with good accuracy. Though both these methods showed good robustness to sensor noise, the structural method has significant benefits: (1) simplicity due to the use of structural models, (2) features such as isolability and detectability can be identified during design stage, and (3) use of rate-of-change of residual to detect faults thereby simplifying the analysis.
Appendix A. Quantitative model The transfer function model of cement pyro-processing obtained using system identification technique is given by
Y1(z ) 0.0928z 3 − 0.2145z 2 + 0.1481z − 0.0264 = 4 U (z ) z − 3.4529z 3 + 4.4152z 2 − 2.4702z + 0.5079 Y2(z ) −1.2928z 3 + 3.0870z 2 − 2.3475z + 0.5541 = 4 U (z ) z − 3.4529z 3 + 4.4152z 2 − 2.4702z + 0.5079 Y3(z ) −21.5222z 3 + 54.0232z 2 − 44.2539z + 11.7542 = 4 U (z ) z − 3.4529z 3 + 4.4152z 2 − 2.4702z + 0.5079 The model is obtained using kiln fuel rate as input variable, and pre-heater inlet oxygen, kiln inlet temperature and burning zone temperature as output variables. The models were obtained by applying a Pseudo Random Binary Sequence (PRBS) signal during the experiments. The identified models showed an accuracy of 85.32%, 84.68%, and 84.93% for the three transfer function models.
Appendix B. Residual calculation B.1. Quantitative model This section illustrates the procedure to compute the residual of the qualitative and quantitative models. The residual for the quantitative model is defined as the difference between the actual output of the plant and the model of the plant. The plant model for the three sensors is given in Appendix A. The output of the sensor transfer functions for a given kiln fuel rate U is compared with the actual plant output and the residual is used to detect the error for the quantitative model. B.2. Structural model The rate of change of residual is used in the structural model for the three sensors and is given by,
R1 = ΔY1 − [G11ΔU1 + G12ΔU2 + G13ΔU3] R2 = ΔY2 − [G21ΔU1 + G22ΔU2 + G23ΔU3] R3 = ΔY3 − [G31ΔU1 + G32ΔU2 + G33ΔU3] where ΔY1, ΔY2, and ΔY3 are the rate of change of output of kiln sensors, ΔU1, ΔU2, and ΔU3 denote the rate of change of kiln inputs, and Gij are the gains related to the respective change in output to change in input as described in the paper. One can verify that the rate of change of residual is used to compute the faults in structural analysis, whereas the residual is directly used in quantitative methods. Note: Noise and faults were included in the plant (ΔY1, ΔY2, ΔY3).
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References Blanke, M., & Lorentzen, T. (2006). Satool—a software tool for structural analysis of complex automation systems 1, 2. In IFAC proceedings, Beijing, China (Vol. 35 (13), pp. 629–634). Blanke, M., Staroswiecki, M., & Wu, N. E. (2001). Concepts and methods in faulttolerant control. In Proceedings of the 2001 American control conference, 2001 (Vol. 4, pp. 2606–2620). Arlington, VA, USA: IEEE. Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., & Schröder, J. (2006). Diagnosis and fault-tolerant control (Vol. 2). Berlin: Springer. Blesa, J., Puig, V., Romera, J., & Saludes, J. (2011). Fault diagnosis of wind turbines using a set-membership approach. IFAC Proceedings 44 (1) 8316–8321. Blesa, J., Rotondo, D., Puig, V., & Nejjari, F. (2014). Fdi and ftc of wind turbines using the interval observer approach and virtual actuators/sensors. Control Engineering Practice, 24, 138–155. Correcher, A., Morant, F., Garcia, E., Blasco-Gimenez, R., & Quiles, E. (2002). Failure diagnosis of a cement kiln using expert systems. In IECON 02. IEEE 2002 28th annual conference of the Industrial Electronics Society. (Vol. 3, pp. 1881–1886). Sevilla, Spain: IEEE. Daniali, M. (2013). Fault-diagnosis of a controlled pyro-process (Master of Engineering Thesis). Technical University of Denmark & FLSmidth. Falkenberg, T., Gregersen, R. T., & Blanke, M. (2014). Navigation system fault diagnosis for underwater vehicle. IFAC Proceedings 47 (3) 9654–9660. Gao, Z., Cecati, C., & Ding, S. X. (2015b). A survey of fault diagnosis and fault-tolerant techniques. Part i: fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757–3767. Gao, Z., Cecati, C., & Ding, S. X. (2015a). A survey of fault diagnosis and fault-tolerant techniques. Part ii: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Transactions on Industrial Electronics, 62(6), 3768–3774. Hovgaard, T. G. (2009). Active sensor configuration validation in refrigeration systems (Ph.D. thesis). Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark. Izadi-Zamanabadi, R., & Blanke, M. (1999). A ship propulsion system as a benchmark for fault-tolerant control. Control Engineering Practice, 7(2), 227–239. Kadri, O., Mouss, L. H., & Mouss, M. D. (2012). Fault diagnosis of rotary kiln using svm and binary aco. Journal of Mechanical Science and Technology, 26(2), 601–608. Knüppel, T., Blanke, M., & Østergaard, J. (2014). Fault diagnosis for electrical distribution systems using structural analysis. International Journal of Robustness and Nonlinear Control, 24(8–9), 1446–1465. Liu, Z., Ahmed, Q., Zhang, J., Rizzoni, G., & He, H. (2016). Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications. Control Engineering Practice, 52, 46–58. Mahdaoui, R., Mouss, L. H., Mouss, M. D., & Chouhal, O. (2011). A temporal neurofuzzy monitoring system to manufacturing systems, arXiv:1107.3302. Makaremi, I., Fatehi, A., Araabi, B. N., Azizi, M., & Cheloeian, A. (2009). Abnormal condition detection in a cement rotary kiln with system identification methods. Journal of Process Control, 19(9), 1538–1545. Sadeghian, M., & Fatehi, A. (2011). Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique. Journal of Process Control, 21(2), 302–308. Sivanandam, V., Kannan, R., Srinivasan, S., & Muralidharan, G. (2016). Comparison of subspace and prediction error methods of system identification for cement grinding process. International Journal of Simulation and Process Modelling, 11(2), 97–107. Sørensen, K. L., Blanke, M., & Johansen, T. A. (2015). Diagnosis of wing icing through lift and drag coefficient change detection for small unmanned aircraft. In Proceedings of the 9th IFAC symposium on fault detection, supervision and safety of technical processes, Paris. F.A. Systems (2015). Process flow in cement industries. Technical report. FLSmidth A/ s. Talebnezhad, N., Fatehi, A., & Shoorehdeli, M. A. (2013). Fault detection and isolation of a cement rotary kiln using fuzzy clustering algorithm. In 2013 13th Iranian conference on fuzzy systems (IFSC) (pp. 1–6). Qazvin, Iran: IEEE. Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part ii: qualitative models and search strategies. Computers & Chemical Engineering, 27(3), 313–326. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis: Part iii: process history based methods. Computers & Chemical Engineering, 27(3), 327–346. Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part i: quantitative model-based methods. Computers & Chemical Engineering, 27(3), 293–311. Venkatesh, S., Ramkumar, K., Guruprasath, M., Srinivasan, S., & Balas, V. E. (2016). Generalized predictive controller for ball mill grinding circuit in the presence of feed-grindability variations. Studies in Informatics and Control, 25(1), 29–38. Willersrud, A., Blanke, M., & Imsland, L. (2015). Incident detection and isolation in drilling using analytical redundancy relations. Control Engineering Practice, 41, 1–12. Zhang, X. 2010. Structural analysis for the diagnosis of two-tank system. In Proceedings of the 5th international conference on pervasive computing and applications.
Contents lists available at ScienceDirect
Control Engineering Practice journal homepage: www.elsevier.com/locate/conengprac
Structural analysis based sensor measurement fault diagnosis in cement industries V. Gomathi b, Seshadhri Srinivasan a,n, K. Ramkumar b, Guruprasath Muralidharan c a
Berkeley Education Alliance for Research in Singapore, Singapore 138602, Singapore School of Electrical and Electronics Engineering, SASTRA University, India c Smarta Opti Solutions, Chennai, India b
art ic l e i nf o
a b s t r a c t
Article history: Received 4 August 2016 Received in revised form 23 February 2017 Accepted 25 February 2017
This investigation presents a fault diagnosis methodology for detecting sensor faults in cement industries pyro processing section. It works in three steps: (a) modelling, (b) analysis, and (c) validation. In the modelling, the actual data from the cement pyro processing is used to do a correlation analysis between output and input variables. The structural model is obtained from the correlation tests. During the analysis phase the Structural analysis Tool (SaTool) is used to detect the detectability and isolability of the faults. The results of the structural analysis are validated in a cement industry using residual analysis performed using structural sensor model and real-time measurements. The main advantages of this fault diagnosis technique are: (a) it requires only correlation analysis to obtain the structural model without a detailed physical model as in other methods, (b) conclusions regarding detectability and isolability can be easily drawn during the analysis stage itself, and (c) the method is simple compared to the model-based, and data-history based methods. The effectiveness of the proposed method is illustrated using data from cement pyro processing plant and its performance is compared with model based approaches for four different types of sensor faults: (1) bias, (2) drift, (3) stuck, and (4) measurement failures. Our results demonstrate that the structural method is able to detect the sensor faults even in the presence of noisy information, and its performance is comparable with that of model based approaches without employing a physical model. & 2017 Published by Elsevier Ltd.
Keywords: Fault diagnosis Structural model Measurement faults Cement kiln Structural analysis SaTool
1. Introduction Sensor measurement faults are frequent in pyro processing section of cement industries due to hazardous operating conditions, improper purging and location of sensors, contamination, and material deposition. Early detection of measurement faults in pyro processing improves production and product quality in cement industries. Furthermore, they are important for avoiding abnormal and unsafe operating conditions. As detecting measurement faults requires continuous monitoring, complete automation of the task is necessitated and fault diagnosis systems are used to this extent. Traditionally, in cement industries fault diagnosis systems are integrated with advanced control systems for building fault tolerant control. Realizing this importance, scientists and researchers n
Corresponding author. E-mail addresses: [email protected] (V. Gomathi), [email protected] (S. Srinivasan), [email protected] (K. Ramkumar), [email protected] (G. Muralidharan). http://dx.doi.org/10.1016/j.conengprac.2017.02.012 0967-0661/& 2017 Published by Elsevier Ltd.
have devoted significant attention in designing fault diagnosis schemes in cement industries. The available approaches can be broadly classified into four categories, they are: (i) quantitative model based methods (Gao, Cecati, & Ding, 2015a; Venkatasubramanian, Rengaswamy, & Yin, 2003), (ii) process history based approaches (Venkatasubramanian, Rengaswamy, Kavuri, & Yin, 2003), (iii) qualitative methods (Venkatasubramanian, Rengaswamy, Kavuri, 2003), and (iv) hybrid approaches (Gao, Cecati, & Ding, 2015b). Quantitative model based require complex mathematical description of dynamic process that are difficult to obtain in practice (Sadeghian & Fatehi, 2011). This makes their adaptation in cement industries difficult. Process history based approaches use past information collected from the plant and soft-computing tools such as artificial neural networks, fuzzy or hybridization of these techniques to build a fault detection scheme (Makaremi, Fatehi, Araabi, Azizi, & Cheloeian, 2009; Mahdaoui, Mouss, Mouss, & Chouhal, 2011). The investigation in Makaremi et al. (2009) studied the detection of super chilling and super-heating of kiln using locally linear neuro fuzzy model. The investigation used motor power consumption to
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Fig. 1. Process flow in cement plant (F. A. Systems, 2015).
monitor the abnormal conditions inside the kiln. A fuzzy inference based fault diagnosis method for detecting faults was proposed in Correcher, Morant, Garcia, Blasco-Gimenez, and Quiles (2002). More recently, Talebnezhad, Fatehi, and Shoorehdeli (2013) used fuzzy clustering algorithm for fault detection and isolation in cement kilns. The investigation used signal-based methods and three clustering algorithms to design fault diagnosis technique. The role of neuro fuzzy approaches for detecting actuator faults have been studied in Mahdaoui et al. (2011). Further, the investigation studied the generalization and reasoning capability. In process history based methods, selection of the classification and clustering tool, generalization to various faults, and the presence of only finite sampling of the distribution of the class of measurement data space are major drawbacks that make adaptation in industries difficult. Hybrid approach that combined support vector machines (SVM) and binary ant colony optimization for rotary cement kiln has been studied in Kadri, Mouss, and Mouss (2012). Still, the selection of SVM as a classifier due to its accuracy cannot be generalized for a wide class of sensor faults. More recently, structural approach for detecting faults in cement kilns was studied in Daniali (2013). The structural approach uses an abstract model leading to significant simplicity of the fault diagnosis scheme. Furthermore, important properties of the fault diagnosis such as detectability and isolability can be inferred from structural analysis. Structural approach has been used for various applications such as refrigeration systems (Hovgaard, 2009), wind turbines (Blesa, Puig, Romera, & Saludes, 2011; Blesa, Rotondo, Puig, & Nejjari, 2014), electrical distribution systems (Knüppel, Blanke, & Østergaard, 2014), vehicle navigation (Falkenberg, Gregersen, & Blanke, 2014), unmanned air-craft systems (Sørensen, Blanke, & Johansen, 2015), lithium-ion batteries (Liu, Ahmed, Zhang, Rizzoni, & He, 2016), ship propulsion system (Izadi-Zamanabadi & Blanke, 1999) and chemical systems (Zhang, 2010). To our best knowledge, the role of structural analysis has not been extensively studied in pyro processing. Motivated by this research gap, this investigation aims to develop a fault diagnosis scheme for pyro processing by combining structural analysis, process simulations, and field experiments. Departing from traditional quantitative approaches, correlation analysis on input–output data from either real-experiment or
process simulations that mimic actual conditions can be used to build structural model for analyzing sensor faults. Then residual between the sensor structural model and actual plant data from a cement pyro processing is used to validate the structural analysis. The validated sensor models are then used for building reliable fault-tolerant controllers for pyro processing. The advantages of the method are: need for only correlation data as against detailed physical models in quantitative approaches, ability to provide insights on desirable features such as detectability and isolability during design stage, and the simplicity of the fault diagnosis scheme. Main contributions of the investigation are: (i) a new fault diagnosis design technique that combines process/simulation data, structural model, and experimental measurements, (ii) a workflow for designing reliable fault diagnosis scheme using structural analysis for cement pyro processing, and (iii) validation of the proposed scheme in a cement industry. The paper is organized into five sections. Section 2 describes the cement manufacturing and the cement kiln process. In addition the motivation for building the fault diagnosis scheme based on structural model is also discussed. The proposed approach and the work flow for building fault diagnosis schemes for cement kilns is presented in Section 3. Validation of the fault diagnosis scheme using industrial measurements is illustrated in Section 4. Conclusions and future prospects of this investigation are discussed in Section 5.
2. Problem formulation The cement manufacturing consists of three stages: raw mix preparation, pyro processing, and grinding as shown in Fig. 1. The raw materials quarried from the mines are crushed and blended with additives in a predefined proportion to be stored in silos. In pyro processing, the raw material is converted to clinkers at around 100 °C in three steps: pre-heating, calcination and burning as shown in Fig. 2. During pre-heating 70% of pre-calcination is performed at a temperature of 900 °C, followed by de-carbonation in calciner. The calcined feed (1000 °C) is given to higher end of the rotary kiln and fuel is fed at the lower end. The material in the
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Fig. 2. Schematic of clinker process (F. A. Systems, 2015).
kiln is burned to very high temperature and the inclination of the kiln is used to move the material from higher to lower end at a temperature of around 1450 °C. The output thus obtained is passed on to grate cooler, where the clinker is formed at a temperature of around 100 °C. The last stage is the cement grinding circuit that takes the clinkers and produces powdered cement in correct ratio. The pyro processing stage uses various sensors such as oxygen analyzers, temperature measuring devices in the cyclones and burning zone area that are subjected to frequent faults and may completely fail due to the environment. The faults can be broadly classified as: (1) Process faults which may occur due to the problem with mechanical components or other operating changes. (2) Sensor/measurement faults due to failures in sensors. Sensor fault/failure may occur as they are placed in a dusty environment and sometimes subject to manual interventions. Such failures also influence the product quality, and throughput of the cement industry. Therefore, early detection of such faults is important for operating the cement industry in a reliable and efficient manner. As pyro processing is a complex process and building mathematical models describing their behavior is cumbersome and it limits the possibility of using quantitative approaches for faultdiagnosis. History based approaches require data from different sensors for various faulty conditions. Furthermore, their generalization to deal with different sensor faults is rather difficult. In this backdrop, qualitative approaches based on structural models provide a simpler way to build fault-diagnosis schemes. One of the shortcomings of the qualitative approach is the generation of spurious signals (Venkatasubramanian, Rengaswamy, Yin, et al., 2003) and it can be eliminated by validating the qualitative approach with sensor measurements obtained from cement kiln. Moreover, structural analysis reveals important features (e.g. detectability) of the fault-diagnosis scheme during design stage itself, whereas other methods require residual analysis to draw conclusions on these features. This investigation first uses simulation on simple process models to obtain data useful for studying correlation between sensor measurements and system variables. The correlation analysis results are then used to choose the process variables to obtain structural model that is followed by structural analysis to identify desirable properties of fault diagnosis schemes during the analysis stage. The SaTool (Structural analysis Tool) a Matlab based package developed by Blanke and Lorentzen (2006) and Willersrud, Blanke, and Imsland (2015) is used to perform structural analysis. To
validate the results, residual analysis between structural sensor model and real-time measurement data from the cement industry obtained using FLSmidth Pvt. Ltd., supervisory control and data acquisition (SCADA) systems is used.
3. Measurement fault diagnosis in cement industries 3.1. Work-flow for building FDI The structural analysis scheme has three phases: Modelling, Analysis, and Validation as shown in Fig 3. The various stages are detailed below. 3.2. Modelling 3.2.1. Data collection The first step in building the fault diagnosis scheme is performing experiments/simulations to obtain sensor data and process variables using ECS/CEMulator1 which is a software that mimics the actual plant operations. A screen shot of the cement process is shown in Fig. 4. The structural model is obtained using process knowledge gathered from simulations performed on CEMulator and correlation tests on sensor data with process variables. The input variables of the simulator are kiln feed rate U1, kiln fuel rate U2, and kiln speed U3, whereas the sensor measurements pre-heater inlet oxygen Y1, kiln inlet temperature Y2 and burning zone temperature Y3, respectively. Variables U and Y denote the input and output variables of the cement kiln system. Variations of the kiln speed, kiln fuel rate, kiln feed rate and sensor measurements are recorded from the simulator as shown in Figs. 5–7, respectively. These variables are selected using studies on ECS/CEMulator and recording the sensor data. The data can also be used to obtain a transfer function model (Sivanandam, Kannan, Srinivasan, & Muralidharan, 2016; Venkatesh, Ramkumar, Guruprasath, Srinivasan, & Balas, 2016). The transfer function model obtained from the data is provided in Appendix A. Clearly, the model can be used for repeating the approach described in the paper. 1
ECS is a proprietary simulator of FLSmidth Pvt. Ltd.
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Fig. 3. Work-flow for building FDI in cement kilns.
3.2.2. Correlation analysis In the second step, the recorded data (sensor measurements w. r.t. inputs) from the CEMulator is subjected to a correlation analysis, wherein Pearson's correlation coefficient denoted by
−1 ≤ r ≤ 1 quantitatively describes the dependencies of the sensor measurement on system variables. As sensor measurements are recorded based on cement industry ambiance, noise and disturbances are reflected by the CEMulator and are included
Fig. 4. Screen shot of the pyro processing in ECS/CEMulator.
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inherently in correlation analysis. The correlation coefficient recorded for the inputs and outputs considered is shown in Table 1 and the operating points used for experiments in ECS/CEMulator are shown in Table 2. Results of the correlation test are used to choose the process variables to build the structural model, wherein the constraints define the relation between the system variables with sensor measurements. The structural model is a functional abstracted model that eliminates the need to know the complex process dynamics. It uses correlation (functional relation) between sensor measurements and system variables for capturing the dynamics required for fault diagnosis. The structural analysis uses the structural model of a system, represented in a bipartite graph showing the links between variables, parameters and constraints
(Hovgaard, 2009). It simplifies the complex dynamical system to a low level simple model of the system behavior (Blanke, Kinnaert, Lunze, Staroswiecki, & Schröder, 2006; Blanke, Staroswiecki, & Wu 2001). The analysis reveals useful information on which components of the system are subjected to faults and can be easily detectable or isolable. 3.2.3. Structural model The third step defines the structural model, wherein the constraints C that qualitatively relate the known and unknown variables are formulated. They define the correlations between sensor measurement and system variables. Let us define:
Z=κ∪χ
(1)
9.7 9.65 9.6
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500
600
700
800
1020 1010 1000 0
1550 1500 1450 0
3.4 3.2 3
0
No of Samples Fig. 6. Variations in measurements for change in kiln fuel rate.
Kiln inlet o temperature( C) Kilnfeedrate(tph)
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405
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800
900
0
100
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700
800
900
1015 1010 1005
Burning zone o temperature( C)
1520
Pre-heater inlet oxygen(%)
1000
3.5
1500 1480 1460
3
2.5
No of Samples Fig. 7. Variations in measurements for change in kiln feed rate.
Table 1 Correlation between input and output variables.
Table 3 Notation.
Pre-heater Kiln inlet Burning zone inlet oxygen (%) temperature (°C) temperature (°C) Feed rate (tonnes/ hour) Fuel rate (tonnes/hour) Speed in rpm
0.7520631
0.8463395
0.6786097
0.90146 0.75448806
0.913 0.917706251
0.53072 0.822512698
Symbol Definition
Table 2 Operating points: inputs.
400 (tonnes/hour) 9.7 (tonnes/hour) 3.2 (rpm)
where Z is the set of all known and unknown variables, κ the set of known variables and χ the set of unknown variables. Here the set of known variables are κ = {U1, U2, U3, Y1, Y2, Y3} and unknown variables, χ = {ZFE , ZFU , ZO2, ZKIT , ZBZT , ZSP }. The constraints are given as:
C1:
ΔU1 ≡ ZFE
C2:
ΔU2 ≡ ZFU
C3:
ΔU3 ≡ ZSP
C4 :
ΔY1 ≡ ZO 2
C5:
ΔY2 ≡ ZKIT
C6:
ΔY3 ≡ ZBZT
C7:
ZO 2 ≡ G11ZFE + G12ZFU + G13ZSP
C8:
ZKIT ≡ G21ZFE + G22ZFU + G23ZSP
C9:
ZBZT ≡ G31ZFE + G32ZFU + G33ZSP
Kiln speed (in rpm) Kiln feed rate (tonnes/ hour) Kiln fuel rate (tonnes/hour) Pre-heater inlet oxygen %
ZKIT ZBZT
Kiln inlet temperature in °C Kiln burning zone temperature °C
Disturbance terms are added in measurements
Input variable Operating point Feed rate Fuel rate Speed
ZSP ZFE ZFU Z O2
represents the relative gain which denotes the change in output to that of the change in input, U1, U2, U3, Y1, Y2, Y3 are the control inputs and outputs, Δ represents the rate-of-change of the variable, respectively. Constraints 1–3 give the relation between control inputs U and physical inputs Z i.e., ZFE , ZFU , ZSP . While constraints 4–6 relates the controlled variables (Y) and physical outputs ZO2, ZKIT , ZBZT , respectively. The sensor models of oxygen analyzer (ZO2), thermocouple (ZKIT ), and pyrometer (ZBZT ) are modelled using constraints 7, 8 and 9, respectively. Constraint 7 gives oxygen analyzer model which maps ZO2 to ZFE , ZFU , ZSP with the relative gain of G11, G12, G13. Constraint 8 gives thermocouple sensor model which maps ZKIT to ZFE , ZFU , ZSP with the relative gain G21, G22, G23. Constraint 9 gives pyrometer sensor model that maps ZBZT to ZFE , ZFU and ZSP with the relative gains G31, G32, G33. 3.3. Analysis
(2)
where Z represents the physical variable shown in Table 3 and G
3.3.1. Incidence matrix The incidence matrix represents the relationship between the constraints and variables, which are arranged in rows and columns as shown in Table 4. The numeric 1 represents the link between the constraints and variables. The variables U1–Y3 are known, whereas the last 6 columns in the incidence matrix represent the unknown variables.
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Table 4 Incidence matrix.
C1 C2 C3 C4 C5 C6 C7 C8 C9
Table 7 Dependency matrix for Matching 1.
ΔU1
ΔU2
ΔU3
ΔY1
ΔY2
ΔY3
ZFE
ZFU
ZSP
Z O2
ZKIT
ZBZT
1 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0 0
0 0 0 0 0 1 0 0 0
1 0 0 0 0 0 1 1 1
0 1 0 0 0 0 1 1 1
0 0 1 0 0 0 1 1 1
0 0 0 1 0 0 1 0 0
0 0 0 0 1 0 0 1 0
0 0 0 0 0 1 0 0 1
r1 r2 r3
C4
1 1 1
1 1 1
1 1 1
1
C5
C6
C7
C8
C9
1 1
1 1
1
C7(C1(U1), C2(U2), C3(U3), C4(Y1)) = 0
ZFU
ZSP
Z O2
ZKIT
ZBZT
1 0 0 0 0 0 1 1 1
0 1 0 0 0 0 1 1 1
0 0 1 0 0 0 1 1 1
0 0 0 1 0 0 0 0 1
0 0 0 0 1 0 0 1 0
0 0 0 0 0 1 1 0 0
3.3.2. Canonical decomposition Using canonical decomposition method the constraints and variables are rearranged in order to divide the system into three – over constrained, just constrained and under constrained system as shown in Table 5. Redundancy will exist only in over constrained systems. Over constrained system are denoted by
(3)
3.3.3. Complete matching Matching is the procedure of assigning every unknown variable (χ) to each of the constraints. A matching is said to be Complete, if there exists an onto mapping between unknown variables χ and set of all constraints. It is Incomplete if there are unmatched constraints and they are used for deriving the analytical redundancy relation (ARR) which denotes the time evolution of known variables (κ) when the system operates in normal condition. It gives the relation among the constraints and residuals that are generated using backtracking method. The fault diagnosis technique checks whether the ARR is satisfied during each time epoch. In the event of a fault κ fails to satisfy the ARR that meets the following important properties: 1. It should not respond to any unknown inputs and parameters. 2. It should be sensitive to the faults. For the process under study, the Structural analysis Tool (SaTool) was used to perform complete matching for 86 different combinations. We have considered one of the matching in our analysis shown in Table 6. From the table it is observed that the 6 unknown variables were matched with constraints C1 to C6, whereas the constraints C7 to C9 are used to compute the residuals:
(4)
3.3.4. Detectability and isolability A close inspection of the constraints in (2) reveals that the constraints C7, C8 and C9 depend on the unknown variables χ. The first six constraint equations C1–C6 with known variables κ are used to replace the right hand side of the constraint equations in the unmatched constraints C7 to C9. This procedure is repeated until all unmatched constraints are replaced with known variables. This gives the ARR or parity equations (4). The parity equation can be used to draw useful insights into two important features of the fault-diagnosis scheme namely detectability and isolability. Detectability is the ability of the fault diagnosis scheme to identify a particular fault, whereas isolability denotes the capability to identify the component that caused the fault. The dependencies of the constraints on the residuals of the parity equations is modelled using dependency matrix. The columns represent the constraints while the rows capture the residuals. An entry of 1 in a column denotes the effect of the residual on the corresponding constraint. The dependency matrix obtained using parity equations is shown in Table 7. Lemma 1. Blanke et al., 2006). A violation of a constraint C is structurally detectable iff, it has a nonzero Boolean signature in some residual r
C ∈ Cdetectable ⇔ ∃ r: C ≠ 0 ⇒ r ≠ 0
(5)
Lemma 2. Blanke et al., 2006). A violation of a constraint Ci is structurally isolable if and only if, it has a unique signature in the residual vector, i.e. column mi of M is independent of all other columns in M:
C ∈ Cisolable ⇔ ∀ j ≠ i: mi ≠ mj
(6)
The detectability of the fault can be inferred from Lemma 1. In principle it means that, if the residuals r1 to r3 have a Boolean signature for at least one of the residuals then the fault is detectable. From the dependency matrix in Table 7, each constraint has at least one residual having Boolean signature 1 in one of the constraints. Therefore, we can infer that the constraints are detectable from the residuals. These constraint equations are used to build the sensor models (described later) and it captures the rateof-change of residual for a given change in input. Thus, if the constraints can be detected, then the faults involving these corresponding constraints can also be detected from the sensor models. Similarly, the faults are isolable if the columns of dependency matrix are independent. One can observe that the Table 8 Structural property matrix for Matching 1.
Table 6 Matching 1. Constraints Variables
C3
C8(C1(U1), C2(U2), C3(U3), C5(Y2)) = 0
ZFE
S + = |Z +| < |C +|
C2
C9(C1(U1), C2(U2), C3(U3), C6(Y3)) = 0
Table 5 Canonical decomposition.
C1 C2 C3 C4 C5 C6 C9 C8 C7
C1
C1 ZFE
C2 ZFU
C3 ZSP
C4 Z O2
C5 ZKIT
C6 ZBZT
C7 0
C8 0
C9 0
Property Detectability Isolability
C1 D N
C2 D N
C3 D N
C4 D N
C5 D N
C6 D N
C7 D N
C8 D N
C9 D N
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sensor faults are detectable, but as there are dependent columns in the dependency matrix they are not isolable (Table 8). 3.4. Validation In the validation stage, the structural sensor model is compared with actual sensor measurements to generate residuals. 3.4.1. Sensor model To validate the structural approach actual sensor measurements are equated with the structural model of the sensor. The sensor models are obtained from constraints C7, C8, C9 . The sensor models proposed will be used in structural analysis for detecting faults. As will be seen later, quantitative methods will require exact mathematical description for modelling the system. 3.4.2. Residual analysis In the last step, the residuals that are difference between the output of the structural sensor model and the actual measurements are studied. The deviation of the residuals indicates that there are faults. We used a threshold for the bias fault <4% of the nominal value of the sensor measurement. This means if the deviation is ≥4% then a bias fault is concluded. However, our experiments revealed that such thresholds are not required for other faults. This deviation is only due to sensor fault and not due to the set-point changes given by the operator as the residual signal encapsulates the set-point change in both the sensor measurements and structural model.
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structural method uses just the correlation information between sensor data to obtain the structural model. Though both these methods use residual analysis for fault detection, the structural method uses the rate-of-change of the residual as against the actual residual used in quantitative techniques. This provides significant simplicity as it reflects directly the occurrence of the fault i.e. there is significant deviation in output for a small or no change in input. To study the robustness and performance of the methods to different sensor faults considered in our analysis, the experiments were repeated. During our experiments the noise levels were added to the data purposefully to study the robustness and the findings are reported in the subsequent parts of this section. 4.3. Methodology The methodology used for our experiments has four steps:
This section illustrates the structural method applied to fault diagnosis in pyro processing based on experimental data obtained from cement industry and compares it with model based approach. A comparison of fault-diagnosis capability, engineering simplicity, and robustness is taken up for analysis.
1. Plant data collected from CEMulator was used to build the qualitative model using correlation analysis as described in Section 3.1. 2. In the second step, the structural model is obtained by performing correlation analysis between the input and output variables. Initially, the correlation test selects the variables for constructing the structural model and highly correlated variables are chosen for the analysis. In our case, all the three input variables have a high correlation with the output variables. So the three input–output variables are used for building the sensor model for the structural method. The relative gains Gij maps the changes in the input variables to the output of the sensor model. With the abuse of terminology used in structural analysis, this investigation uses the structural model to denote the sensor model in the presentation of the analysis. 3. The rate of change of residual is used for structural analysis, whereas the residual is computed for quantitative method. 4. Conclusions are drawn on the faults based on the residual analysis.
4.1. Sensors and fault classification
4.4. Quantitative methods
Choice of the sensor, and the type of fault is important for validating the fault-diagnosis scheme. This investigation chooses kiln inlet temperature measured by a thermocouple, burning zone temperature obtained from a pyrometer, and pre-heater inlet oxygen measured using an analyzer as the sensors. The choice is dictated by the influence of these sensors on cement production and product quality as closed loop controllers are mainly dependent on them. The following sensor faults are considered in our analysis:
To evaluate the performance of quantitative methods, the residuals between the transfer function model and the plant data for the four faults (bias, drift, stuck and measurement failure) are considered. We present the analysis of different faults by compressing the time for non-faulty periods. The time instants mentioned below are scaled for clarity of presentation and do not represent actual time-instants. However, they are indicative of the faults in terms of amplitude and the ratio of normal working to faulty condition. Fig. 8(a) shows the residual signal of the kiln inlet temperature subjected to bias (between 100 and 150 samples), and drift (from 400 to 500 samples). During 100 to 150th sample, the residual signals show a constant amplitude of 5% which indicates a bias fault. Similarly, the residual signal shows a gradual deviation from zero in the 400–500 samples. This denotes the existence of a drift fault. The fault diagnosis system raises an indication about the faults to the advanced process control layer for taking corrective actions. Similarly, the pyrometer residual obtained using quantitative model for different faulty conditions are studied. Fig. 8(b) shows the performance of the method for bias fault (from 300 to 350 samples), and sensor stuck (from 500 to 550 samples). A residual signal of 5% constant amplitude represents a bias fault, whilst a very small deviation from the actual value but not changing with time indicates that it is a sensor stuck. The quantitative method was also used to study the oxygen
4. Results
1. Bias fault wherein a constant amplitude signal is added to the measured value due to deposition of dust or a coating on the sensor. 2. Drifts are the deviations of the measurements from the actual value due to ageing, noise and other factors. 3. Sensor stuck occurs due to manual or signal interruption and the sensor measurement is stuck to its previous value. 4. Sensor measurement failure is the absence of measurements due to failure of connecting probes or wire breakage. 4.2. Description of the experiment To illustrate the advantages of structural method, two sets of tests are reported: (i) quantitative model based approach and (ii) qualitative structural approach. While the quantitative method relies on the physical model (e.g. transfer function model), the
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6
(a)
Kiln inlet temperature
4
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Residual signal (%)
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2 0
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(c)
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700
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Bias fault
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0
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Preheater inlet oxygen
50
0
200
600
700
800
No of samples Fig. 8. Residual in quantitative method: (a) kiln inlet temperature, (b) burning zone temperature, (c) pre-heater inlet oxygen.
analyzer. The residual signals obtained for bias (between 200 and 250th sample and from 500 to 550 sample) and sensor measurement failure (between 600 and 650th sample) are reported in Fig. 8(c). One can verify that the residual signal with constant amplitude of 5% and 10% is noticed during the different time-instants. This indicates the presence of the bias fault. Similarly, change in residual from zero to 100% between time-instants 600– 650 sample) means there is a 100% fault in measurements and it can be concluded that it indicates a complete sensor measurement failure. 4.4.1. Robustness analysis To study the robustness of the sensor faults to noise, residual
signals under noisy conditions were analyzed. It was found that the oxygen analyzer is not subjected to noise and therefore, was not considered in the analysis. The residuals of the kiln inlet temperature (thermocouple) and burning zone temperature (pyrometer) subjected to bias faults with noise are shown in Fig. 9 (a) and (b), respectively. It can be seen that the thermocouple residual signal deviates from zero at the 100th sample and gradually increases till 200th sample. This indicates a sensor drift and a deviation in the sensor signal between 400 and 450th samples with constant amplitude 5% of nominal value indicates the bias fault. Although, subjected to noise the quantitative method was able to identify the faults clearly. However, an analysis of pyrometer residual signals revealed that bias fault could be identified
6
(a)
Kiln inlet temperature
4
Bias fault Drift fault
Residual signal (%)
2
0
-2
0
100
200
300
400
500
600
700
800
15
(b)
Burning zone temperature
10
Bias fault 5
Sensor stuck
0
-5
0
100
200
300
400
500
600
700
800
No of samples Fig. 9. Residual with sensor noise in quantitative method: (a) kiln inlet temperature and (b) burning zone temperature.
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using the residual analysis (between 300 and 350 sample), the sensor stuck fault was detected as a change in sensor signal (from 400 to 500th sample). The result demonstrates that quantitative methods have significant robustness in identifying bias, drift, stuck, and sensor measurement faults.
shown in Fig. 11. The residual signal changes at the 100th and 130th sample with a constant amplitude of 74% and there is a change in pyrometer residual signals at 400th and 500th sample with a constant magnitude of 75% indicating the presence of a bias fault during the period. The residual signal is not affected by noise since the fault magnitude is higher than that of the noise.
4.5. Structural method
4.7. Multiple faults with noise
In qualitative methods, the rate of change of residual between the structural model and actual data is used for detecting the fault. Here the use of structural method for bias, drift, and sensor measurement failure is presented. Fig. 10(a) shows the rate of change of residual of kiln inlet temperature, from the residual signal one can verify that there is a change in its residual signal with 5% amplitude at the 94th sample and 5% amplitude at 123 sample. These changes in sensor measurement with constant amplitude indicate the presence of a bias fault during those time instants. In addition, the signal also shows that there is a minimum deviation in the residual from zero at the 195th sample till 215th sample. This indicates the presence of drift in the sensor measurement. The use of structural method for studying faults in pyrometer measuring zone temperature is reported in Fig. 10(b). A close inspection reveals that there is a change of residual from 0 to þ100% at the 394th sample, and 100% change in amplitude at 413th sample. These changes reveal the presence of complete sensor measurement failure during such time instant. The signal also shows small deviation during 595 to 625th samples which indicates a drift in sensor measurement. The residual signal of the oxygen analyzer for bias fault is reported in Fig. 10(c). The amplitude of the residual signal changes from 390 to 440 samples, and 550–580 samples from zero to þ10% and 10% and þ4% and 4%, respectively. This indicates the presence of bias fault in the sensor.
Fig. 12 (a) shows the change in residual signal of kiln inlet temperature and burning zone temperature under noisy condition for different faults. There is a change in the residual signal at 100th and 130th sample with the constant amplitude of 74%, so it is observed that there is a bias fault that occurs in thermocouple measurement. The change in residuals also shows deviations of around 2.5% approximately at 200 sample thus indicating the presence of sensor drift. The residual signal of pyrometer sensor showing changes in the residual with 7100% change in amplitude at 390th and 415th sample means that the sensor measurement failure during that time. This signal also shows that there is deviations in the residual with 75% amplitude change during 300 to 350th and 600 to 700th samples. This deviation is higher than the noise magnitude, and it is concluded that bias faults have occurred. The noise signals are not visible in the graph as their magnitude is lesser than residuals. To conclude, both quantitative and qualitative methods were applied to detect faults in cement industry. Our analysis found that while the performance of both these methods were comparable, the qualitative method provided significant simplicity and advantages that is not possible with the quantitative approach. This is mainly because, the qualitative methods used only the correlation analysis as against the accurate physical model required for quantitative method. Furthermore, the structural analysis answers questions regarding the isolability and detectability during the design stage, whereas an actual residual calculation is required to determine these features in quantitative methods. Moreover, as the structural method used rate of change of residual provides simplicity as it directly relates to the occurrence of the fault i.e. an abnormal change in the output for a small/no change in the input. Therefore, qualitative methods provide significant advantages in
4.6. Robustness of structural method The residual signal of thermocouple and pyrometer under noisy condition for the bias fault with noisy measurement is 5
(a)
Kiln inlet temperature
Drift
0
Bias fault
Residual signal (%)
-5
0
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100 50
(b)
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-50
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-100 0 10
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(c)
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900
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5 0
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-5 -10 0
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No of samples Fig. 10. Rate of change in residuals in qualitative method: (a) kiln inlet temperature, (b) burning zone temperature, (c) pre-heater inlet oxygen.
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4 2 0 -2
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-4 -6
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No of samples Fig. 11. Rate of change in residuals with sensor noise in qualitative method: (a) kiln inlet temperature and (b) burning zone temperature.
5
Kiln inlet temperature
Drift
Residual signal (%)
0
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0
100
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100 Burning zone temperature
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50
0
-100
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Bias fault
-50
0
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300
400
500
600
700
800
900
No of samples Fig. 12. Rate of change in residuals with sensor noise in qualitative method: for multiple faults.
building fault diagnosis methods in cement industry.
5. Conclusion This paper presented a fault diagnosis (FDI) technique based on structural method to identify sensor faults in cement industry. The proposed approach combined simulations (in CEMulator), structural model, structural analysis, and experimental data from cement kilns to build and validate the structural FDI scheme. First, experiments were performed in ECS/CEMulator software from FLSmidth Pvt. Ltd. The experimental data was used to build both structural and transfer function model. Correlation analysis and system identification techniques were used to build the structural and transfer function model, respectively. The use of correlation analysis as
against high fidelity physical models provided significant simplicity to the structural method. The structural analysis tool (SaTool) was then used to analyze the structural model to answer questions such as isolability and detectability of the sensor faults. These features can be verified for quantitative methods only after residual analysis. Thus, significant insights into the FDI capability can be ascertained during the design stage itself with the structural analysis. The investigation provided a work-flow that can be used to build a FDI and it consisted of three stages: (1) Modelling, (2) Analysis, and (3) Validation. In the modelling phase the structural model was generated from correlation analysis. The analysis phase used SaTool to answer questions regarding the features of FDI. Finally, in the validation phase the results were validated with actual sensor measurements. To illustrate the advantages of the structural approach the
V. Gomathi et al. / Control Engineering Practice 64 (2017) 148–159
method was compared with quantitative model based approach. The performance of these methods to faults in kiln temperature, burning zone temperature, and oxygen analyzer were studied. The bias, drift, sensor stuck, and sensor measurement failure were analyzed. Our results showed that structural analysis were not able to identify sensor stuck fault. However, the other faults were identified with good accuracy. Though both these methods showed good robustness to sensor noise, the structural method has significant benefits: (1) simplicity due to the use of structural models, (2) features such as isolability and detectability can be identified during design stage, and (3) use of rate-of-change of residual to detect faults thereby simplifying the analysis.
Appendix A. Quantitative model The transfer function model of cement pyro-processing obtained using system identification technique is given by
Y1(z ) 0.0928z 3 − 0.2145z 2 + 0.1481z − 0.0264 = 4 U (z ) z − 3.4529z 3 + 4.4152z 2 − 2.4702z + 0.5079 Y2(z ) −1.2928z 3 + 3.0870z 2 − 2.3475z + 0.5541 = 4 U (z ) z − 3.4529z 3 + 4.4152z 2 − 2.4702z + 0.5079 Y3(z ) −21.5222z 3 + 54.0232z 2 − 44.2539z + 11.7542 = 4 U (z ) z − 3.4529z 3 + 4.4152z 2 − 2.4702z + 0.5079 The model is obtained using kiln fuel rate as input variable, and pre-heater inlet oxygen, kiln inlet temperature and burning zone temperature as output variables. The models were obtained by applying a Pseudo Random Binary Sequence (PRBS) signal during the experiments. The identified models showed an accuracy of 85.32%, 84.68%, and 84.93% for the three transfer function models.
Appendix B. Residual calculation B.1. Quantitative model This section illustrates the procedure to compute the residual of the qualitative and quantitative models. The residual for the quantitative model is defined as the difference between the actual output of the plant and the model of the plant. The plant model for the three sensors is given in Appendix A. The output of the sensor transfer functions for a given kiln fuel rate U is compared with the actual plant output and the residual is used to detect the error for the quantitative model. B.2. Structural model The rate of change of residual is used in the structural model for the three sensors and is given by,
R1 = ΔY1 − [G11ΔU1 + G12ΔU2 + G13ΔU3] R2 = ΔY2 − [G21ΔU1 + G22ΔU2 + G23ΔU3] R3 = ΔY3 − [G31ΔU1 + G32ΔU2 + G33ΔU3] where ΔY1, ΔY2, and ΔY3 are the rate of change of output of kiln sensors, ΔU1, ΔU2, and ΔU3 denote the rate of change of kiln inputs, and Gij are the gains related to the respective change in output to change in input as described in the paper. One can verify that the rate of change of residual is used to compute the faults in structural analysis, whereas the residual is directly used in quantitative methods. Note: Noise and faults were included in the plant (ΔY1, ΔY2, ΔY3).
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References Blanke, M., & Lorentzen, T. (2006). Satool—a software tool for structural analysis of complex automation systems 1, 2. In IFAC proceedings, Beijing, China (Vol. 35 (13), pp. 629–634). Blanke, M., Staroswiecki, M., & Wu, N. E. (2001). Concepts and methods in faulttolerant control. In Proceedings of the 2001 American control conference, 2001 (Vol. 4, pp. 2606–2620). Arlington, VA, USA: IEEE. Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., & Schröder, J. (2006). Diagnosis and fault-tolerant control (Vol. 2). Berlin: Springer. Blesa, J., Puig, V., Romera, J., & Saludes, J. (2011). Fault diagnosis of wind turbines using a set-membership approach. IFAC Proceedings 44 (1) 8316–8321. Blesa, J., Rotondo, D., Puig, V., & Nejjari, F. (2014). Fdi and ftc of wind turbines using the interval observer approach and virtual actuators/sensors. Control Engineering Practice, 24, 138–155. Correcher, A., Morant, F., Garcia, E., Blasco-Gimenez, R., & Quiles, E. (2002). Failure diagnosis of a cement kiln using expert systems. In IECON 02. IEEE 2002 28th annual conference of the Industrial Electronics Society. (Vol. 3, pp. 1881–1886). Sevilla, Spain: IEEE. Daniali, M. (2013). Fault-diagnosis of a controlled pyro-process (Master of Engineering Thesis). Technical University of Denmark & FLSmidth. Falkenberg, T., Gregersen, R. T., & Blanke, M. (2014). Navigation system fault diagnosis for underwater vehicle. IFAC Proceedings 47 (3) 9654–9660. Gao, Z., Cecati, C., & Ding, S. X. (2015b). A survey of fault diagnosis and fault-tolerant techniques. Part i: fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757–3767. Gao, Z., Cecati, C., & Ding, S. X. (2015a). A survey of fault diagnosis and fault-tolerant techniques. Part ii: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Transactions on Industrial Electronics, 62(6), 3768–3774. Hovgaard, T. G. (2009). Active sensor configuration validation in refrigeration systems (Ph.D. thesis). Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark. Izadi-Zamanabadi, R., & Blanke, M. (1999). A ship propulsion system as a benchmark for fault-tolerant control. Control Engineering Practice, 7(2), 227–239. Kadri, O., Mouss, L. H., & Mouss, M. D. (2012). Fault diagnosis of rotary kiln using svm and binary aco. Journal of Mechanical Science and Technology, 26(2), 601–608. Knüppel, T., Blanke, M., & Østergaard, J. (2014). Fault diagnosis for electrical distribution systems using structural analysis. International Journal of Robustness and Nonlinear Control, 24(8–9), 1446–1465. Liu, Z., Ahmed, Q., Zhang, J., Rizzoni, G., & He, H. (2016). Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications. Control Engineering Practice, 52, 46–58. Mahdaoui, R., Mouss, L. H., Mouss, M. D., & Chouhal, O. (2011). A temporal neurofuzzy monitoring system to manufacturing systems, arXiv:1107.3302. Makaremi, I., Fatehi, A., Araabi, B. N., Azizi, M., & Cheloeian, A. (2009). Abnormal condition detection in a cement rotary kiln with system identification methods. Journal of Process Control, 19(9), 1538–1545. Sadeghian, M., & Fatehi, A. (2011). Identification, prediction and detection of the process fault in a cement rotary kiln by locally linear neuro-fuzzy technique. Journal of Process Control, 21(2), 302–308. Sivanandam, V., Kannan, R., Srinivasan, S., & Muralidharan, G. (2016). Comparison of subspace and prediction error methods of system identification for cement grinding process. International Journal of Simulation and Process Modelling, 11(2), 97–107. Sørensen, K. L., Blanke, M., & Johansen, T. A. (2015). Diagnosis of wing icing through lift and drag coefficient change detection for small unmanned aircraft. In Proceedings of the 9th IFAC symposium on fault detection, supervision and safety of technical processes, Paris. F.A. Systems (2015). Process flow in cement industries. Technical report. FLSmidth A/ s. Talebnezhad, N., Fatehi, A., & Shoorehdeli, M. A. (2013). Fault detection and isolation of a cement rotary kiln using fuzzy clustering algorithm. In 2013 13th Iranian conference on fuzzy systems (IFSC) (pp. 1–6). Qazvin, Iran: IEEE. Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part ii: qualitative models and search strategies. Computers & Chemical Engineering, 27(3), 313–326. Venkatasubramanian, V., Rengaswamy, R., Kavuri, S. N., & Yin, K. (2003). A review of process fault detection and diagnosis: Part iii: process history based methods. Computers & Chemical Engineering, 27(3), 327–346. Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part i: quantitative model-based methods. Computers & Chemical Engineering, 27(3), 293–311. Venkatesh, S., Ramkumar, K., Guruprasath, M., Srinivasan, S., & Balas, V. E. (2016). Generalized predictive controller for ball mill grinding circuit in the presence of feed-grindability variations. Studies in Informatics and Control, 25(1), 29–38. Willersrud, A., Blanke, M., & Imsland, L. (2015). Incident detection and isolation in drilling using analytical redundancy relations. Control Engineering Practice, 41, 1–12. Zhang, X. 2010. Structural analysis for the diagnosis of two-tank system. In Proceedings of the 5th international conference on pervasive computing and applications.