The Effect of the Sampling Period on Stiction Detection Methods

Proceedings of the 20th World Congress Proceedings of the 20th World The International Federation of Congress Automatic Control Proceedings of the 20th9-14, World The International Federation of Congress Automatic Control Available online at www.sciencedirect.com Toulouse, France, July 2017 The International Federation of Automatic Control Toulouse, France, July 9-14, 2017 Toulouse, France, July 9-14, 2017

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IFAC PapersOnLine 50-1 (2017) 2848–2853

The Effect of the Sampling Period on Stiction Detection Methods The Effect of the Sampling Period on Stiction Detection Methods The Effect of the Sampling Period on Stiction Detection Methods

Jônathan W. V. Dambros*. Marcelo Farenzena* Jônathan W. V. Dambros*. Marcelo Farenzena* O. Trierweiler* Jônathan W. Jorge V. Dambros*. Marcelo Farenzena* Jorge O. Trierweiler* Jorge O. Trierweiler* * Group of Intensification, Modelling, Simulation, Control and  Optimization of Processes, Chemical Engineering Department, * Group of Intensification, Modelling, Simulation, Control and Optimization of Processes, Chemical Federal University of Rio Grande do Sul (UFRGS), R. Eng. Luiz Englert, s/n, CampusEngineering Central Department, * Group of Intensification, Modelling, Simulation, Control and Optimization of Processes, Chemical Engineering Federal University of Rio Grande do Sul (UFRGS), R. Eng. Luiz Englert, s/n, Campus Central Department, Porto Alegre, Brazil, (e-mail: {dambrosj,R.farenz, jorge}@enq.ufrgs.br) Federal University of Rio Grande do Sul (UFRGS), Eng. Luiz Englert, s/n, Campus Central Porto Alegre, Brazil, (e-mail: {dambrosj, farenz, jorge}@enq.ufrgs.br) Porto Alegre, Brazil, (e-mail: {dambrosj, farenz, jorge}@enq.ufrgs.br) Abstract: Among the causes of oscillation in control loops, stiction in control valves has the highest Abstract: theseveral causesworks of oscillation in control loops, in control valves has the highest incidence. Among There are on stiction detection, but stiction few of them discuss problems related to Abstract: Among theseveral causesworks of oscillation in control loops, stiction in control valves has the highest incidence. ThereFast are on stiction detection, but sampling few of them discuss problems related to industrial data. process dynamics combined with low periods cause problems in the incidence. ThereFast are several on stiction detection, but few of them discuss problems relatedthe to industrial data. processofworks dynamics combined with periods cause problems detection, since the number data per cycle may be lesslow thansampling that required for the analysis. Thisin study industrial data. Fast process dynamics combined with low sampling periods cause problems in the detection, since the of number of data per on cycle be less than that required the analysis. evaluates the effect sampling period fivemay stiction detection methods. Thefor evaluation was This lead study using detection, since the of number of data per on cycle may be less than that required forevaluation the analysis. This study evaluates the effect sampling period five stiction detection methods. The was lead using standard signals and industrial data. The results present the minimum signal sample rate required by the evaluates the effect ofindustrial samplingdata. period five stiction methods. Thesample evaluation was leadby using standard signals and Theonresults presentdetection the minimum signal rate required the selected methods and the lack of techniques for stiction detection in signals with low sampling. standard signals and The results presentdetection the minimum signal sample rate required by the selected methods andindustrial the lack ofdata. techniques for stiction in signals with low sampling. selected methods and the lack of techniques for stiction detection in signals with low sampling. © 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Control valves, stiction detection, oscillation control, fault detection, sampling periods Keywords: Control valves, stiction detection, oscillation control, fault detection, sampling periods Keywords: Control valves, stiction detection, oscillation control, fault detection, sampling periods  

 1. INTRODUCTION 1. INTRODUCTION 1. loop INTRODUCTION Oscillation in control is a problem of high incidence in Oscillationprocesses. in control loop isproblem a problem of high incidence in industrial This causes, among others, Oscillation in control loop isproblem a problem of high incidence in industrial processes. This causes, among others, higher energy costs, decrease in product quality, higher industrial processes. This problem causes, among others, higher energy costs, decrease in on product higher rejection rates, and excessive wear valves quality, (Choudhury et higher energy costs, decrease in on product quality, higher rejection rates, and excessive wear valves (Choudhury et al., 2006). Among the causes of oscillation, the most rejection rates, and excessive wearofon oscillation, valves (Choudhury et al., 2006). Among the causes the most common is stiction in control valvesof(Paulonis e Cox,the2003). al., 2006). Among the causes oscillation, most common is stiction in control valves (Paulonis e Cox, 2003). commonoccurs is stiction in control (Paulonis e Cox, 2003). Stiction when a valvevalves with friction stops or changes Stiction occurs when a valve with friction stops or changes the direction, return with the friction movement, Stiction occurs thus, whento stopsthe or actuator changes the direction, thus, toa valve return the movement, the actuator requires a force higher than usual to overcome the static the direction, thus, to return the movement, the actuator requires aOnce forceovercome, higher than to overcome the static friction. theusual stored potential energy is requires a force higher than usual to overcome the static friction. Once overcome, the stored potential energy is released abruptly as kinetic energy, causing the jump in the friction. Once overcome, the stored potential energy is released abruptly as If kinetic energy, causing the jump in that the valve stem position. the released energy is higher than released abruptly as kinetic energy, causing the jump in the valve stem If theinreleased energy is higher than that required to position. put the valve the desired position, the direction valve stem position. If theinreleased energy is higher than that required to put the valve the desired position, the direction of movement isthe reversed and thedesired valve stops and the sticks again. required to put valve in the position, direction of movement is reversed and the valve stops and sticks again. This phenomenon combined with the use of controller with of movement is reversed and the stops sticks again. This phenomenon combined withvalve the use making ofand controller with integral action causes the limit cycle, the stem This phenomenon combined with the use making of controller with integral action causes the limit cycle, the stem position of the valve varies between two making points, above and integral action causes the limit cycle, the stem position the valve varies between two points, above and below theof desired position. position of the valve varies between two points, above and below the desired position. below the desired position. Considering plants with hundreds or even thousands of Considering plants with hundreds or feasible, even thousands of valves, manual stiction detection is not what makes Considering plants with hundreds or feasible, even thousands of valves, manual stiction detection is not what makes automatic and non-intrusive techniques required. The number valves, manual stiction detection is not feasible, what makes automatic and non-intrusive The number of these techniques is high techniques and, even required. so, new methods are automatic and non-intrusive techniques required. The number of these techniques is high and, even so, new methods are proposed year after year. From the first automatic nonof these techniques isyear. high From and, even so, new methodsnonare proposed year after the first automatic intrusive detection technique created by Horch (1999), at proposed year after year. From the first automatic nonintrusive detection technique created by Horch (1999), at least 30 other methods have been proposed. intrusive detection technique created by Horch (1999), at least 30 other methods have been proposed. least 30 other methods have of been proposed. little is discussed Despite the large number techniques, Despite the largesampling numberrate of required techniques, is detection. discussed about minimum for little stiction Despite the largesampling numberrate of required techniques, little is detection. discussed about minimum forof stiction Some authors present a minimum number points per cycle about minimum sampling rate required for stiction detection. Some authorsdetection present awithout minimum number tests of points perchoice cycle for efficient presenting for the Some authorsdetection present awithout minimum number tests of points perchoice cycle for efficient presenting for the (e.g. He et al (2007)),without but most authors tests do not discuss the for efficient detection presenting for the choice (e.g. He (e.g. et alHorch (2007)), but and mostYamashita authors do not discuss the problem (1999) (2006)). (e.g. He (e.g. et alHorch (2007)), but and mostYamashita authors do not discuss the problem (1999) (2006)). problem (e.g. Horch (1999) and Yamashita (2006)).

As will be discussed in Section 2.1, to detect stiction, many As will be 2.1, to detect stiction, many methods usediscussed the shapein ofSection the signal. Typically, triangular or As will be 2.1, to detect stiction, many methods usediscussed the shapeinthe ofSection the signal. Typically, triangular or square signals indicate presence of stiction and sinusoidal methods use the shape of the signal. Typically, triangular or square signals indicate the presence of stiction and1 sinusoidal signal indicates another cause of oscillation. Fig. shows the square signals indicate the presence of stiction and sinusoidal signal indicates cause of oscillation. Fig. 1 F shows the Fs three signals foranother different sampling period where o and the signal indicates another cause of oscillation. Fig. 1 F shows and Fs three signals for different sampling period where o are respectively the oscillation frequency and the sampling three signals for the different sampling period and where Fsampling o and Fs are respectively oscillation frequency the frequency. From the Fig.oscillation 1 it is seen that for and signals low are respectively frequency the with sampling frequency. From Fig. 1 classification it is seen that for signals with low ≤ 4F ) the between the three types sampling (F s o frequency. From Fig. 1 classification it is seen that for signals withtypes low ≤ 4F ) the between the three sampling (F s o of signal is(Fnot≤ possible since the waveform is the same. 4F ) the classification between the three types sampling s o of signal is not possible since the waveform is the same. of signal is not possible since the waveform is the same.

Fig. 1 – Effect of low sampling on signal waveform. Where Fig. dashed 1 – Effect of are low the sampling on signal waveform. the lines continuous signals and theWhere dots Fig. dashed 1 – Effect of are low the sampling on signal waveform. the lines continuous signals and theWhere dots represent the sampled data. the dashed are data. the continuous signals and the dots represent thelines sampled represent the sampled data. Industrially, the presence of signals with low sampling per Industrially, the presence of signals low sampling per cycle is common, especially for fastwith dynamic loops (e.g. Industrially, the presence of signals low sampling per cycle is common, especially for fastwith dynamic loops (e.g. loops where the flow is the controlled variable). For an cycle is common, especially for fast dynamic loops (e.g. loops wherewith the period flow isequal the controlled variable). For an oscillation to 60 seconds, if sampling loops wherewith the period flow isequal the controlled variable). For an oscillation 60 seconds, sampling period is equal to period 10 seconds, forto example, only 6if data points oscillation with equal to 60 seconds, if sampling period isstored equalper to 10 seconds, for example, only 6 data points will be cycle. The automatic application of stiction period equalper to 10 seconds, for example, only 6 data points will be isstored cycle. The automatic application of stiction will be stored per cycle. The automatic application of stiction

Copyright © 2017, 2017 IFAC 2903Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International Federation of Automatic Control) Copyright 2017 responsibility IFAC 2903Control. Peer review©under of International Federation of Automatic Copyright © 2017 IFAC 2903 10.1016/j.ifacol.2017.08.638

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detection to this signal without the evaluation of sampling period may lead to misleading results. Thus, the aim of this work is to evaluate the applicability of some stiction detection methods to low sampling signals. The study will be specific to fast dynamic processes (flow control) where the effect of sampling is best seen. The remainder of the paper is organized as follows. Section 2 first presents the typical patterns found on control output, process output and valve stem position (OP, PV and MV, respectively) for signals with and without stiction, and then a brief review on stiction detection methods. In section 3, the analysis of the effect of sampling period on five selected detection methods using standard signals are presented. Section 4 presents a brief industrial case analysis for data with low sampling. The conclusions are given in section 5. 2. STICTION PATTERNS AND DETECTION Fig. 2 - Relationship between controller output (OP) and valve stem position (MV).

2.1. Stiction patterns on OP, PV and MV signals The limit cycle caused by stiction has different characteristics compared with those caused by other sources. For stiction case, the speed of the valve stem is kept zero for a period of time, while for the other causes, the limit cycle behavior is usually sinusoidal (Brásio et al., 2014). Process output (PV), valve stem position (MV) and controller output (OP) signals from loops with the presence of valves with stiction have well-defined patterns depending on the process dynamics, stiction level, and controller parameters. Usually, considering stiction as the cause of oscillation, for self-regulating processes, OP and PV signals exhibit triangular and square shapes respectively; for integrating processes, PV signal has triangular shape. For processes where stiction is not the cause of oscillation, the form of the signal is typically sinusoidal in all cases. Fig. 2 shows the typical relationship between the valve stem position (MV) and the controller output (OP) signal for valves with stiction. This relationship consists of three different sections: 

Deadband + stickband – Stuck phase caused by static friction. The OP signal varies while valve position (MV) is kept constant;



Slip-jump – Abrupt change on stem position caused by the relive of potential energy;



Moving phase – After the jump, valve stem moves until it stops again.

Most of the existing stiction detection methods use the classification of the waveform into triangular, square or sinusoidal by OP, PV, or MV signals (e.g. Rossi e Scali (2005), Singhal e Salsbury(2005), He et al (2007), Farenzena e Trierweiler (2012)) or the identification of MV(OP) pattern (e.g. Kano et al. (2004), Yamashita (2006), Scali e Ghelardoni (2008)).

2.2. Stiction detection methods Stiction methods can be divided into four groups: detection of nonlinearity, detection using the standard MV(OP) plot, detection using the waveform and a fourth group where other strategies are used, such as cross-correlation, harmonics location, and combined algorithms. The evaluation of the effect of sampling period for all detection methods would be an exhaustive and extensive work, therefore, for this work, five techniques were selected, three based on the high number of citations in the literature and two for being the most recent techniques during the development of this work. The first technique is also the first non-intrusive stiction detection method proposed. Horch (1999) noted that the phase shift between the controller output (OP) and process output (PV) signals for the case where stiction is the cause of oscillation is approximately equal to 90 degrees, while the phase shift for oscillation caused by an unstable loop or external disturbances is approximately equal to 180 degrees. Thus, the technique applies the cross-correlation between the two signals, if the resulting function is odd, stiction is the cause of oscillation, if even, the cause is other than stiction. Yamashita method (Yamashita, 2006) classifies the direction of movement in MV(OP) plot into I (Increasing), D (decreasing) and S (steady). The sequence of movements is classified into typical sequences for stiction case and general sequences. The relation between these two classifications is used to evaluate the stiction index. Usually stem position data (MV) are not available, but process output (PV) can be used as an approximation for fast dynamic process. The curve fitting method proposed by He et al. (2007) is based on the waveform identification, where each signal halfcycle (limited by zero-crossing values) is fitted to a triangular and sinusoidal functions. Best fit to triangular function indicates the presence of stiction, while best fit to sinusoidal function indicates the presence of another cause of

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oscillation. The fitting is made to PV signal for integrating process and OP signal for self-regulating processes (where the flow is the controlled variable, for example). The work specifies, without proof, minimum value of 15 data points per cycle for the analysis by the method. Dambros et al. (2016) proposed two new methods for stiction detection to signals with variable reference, typically present in industrial data, both based on the waveform identification. The first method differentiates the signal by the peak slope, the slope is calculated for the original signal and for triangular and sinusoidal standard signals with the same frequency of the original. If the slope is closer to the slope of the triangular signal, it indicates the presence of stiction, otherwise the cause of oscillation is another problem. The second method, based on zone segmentation, differentiates the signal by the data distribution, for triangular signal, the distribution of points is uniform, for sinusoidal signal, the points are concentrated in the upper and lower zones. Both methods follow the same criteria used by He, which is, identification is made on OP signal for self-regulating processes and PV signal for integrating processes.

Fig. 3 – Example of signals generated for the analysis of the effect of sampling period, where the dashed lines are the continuous standard signals without noise and the dots represent the sampled data. Table 1. Indexes thresholds for oscillation cause classification for the five selected methods

3. THE EFFECT OF SAMPLING PERIOD ON STICTION METHODS As seen in Fig. 1, the sampling period influences the shape of the signal, and thus influences the stiction identification. In this section, the five selected stiction detection methods are evaluated according to the number of points per cycle for sinusoidal, triangular and square standard signals. The signals generated have the following fixed properties: magnitude equal to 0.5, oscillation period equal to 1 second, and presence of 20 cycles. In order to get a more general result, the phase of the signals is variable and equal to [0, 0.05, 0.1, ..., 0.45] * 2π and random signal with normal distribution and variance equal to [0, 0.02, 0.04, ..., 0.18] is added to the standard signals, the number of points per cycle is equal to [3, 4, 5, ..., 20]. Thus, 1800 sinusoidal, triangular and square signals were generated. Fig. 3 shows a fragment of the signals generated with 5 data per cycle, phase equal to 0.1 * 2π and noise variance equal to 0.04. For the analysis of cross-correlation and Yamashita methods, the presence of stiction is represented by a triangular and a square signal with phase shift equal to 90 degree. On the other hand, the presence of other causes of oscillation is represented by two sinusoidal signals with phase shift equal to 180 degree. For the analysis of curve-fitting methods, peak slope and zone segmentation triangular signal is used for the case with stiction and the sinusoidal signal for the case without the presence of stiction. The analysis for each method is based on four contour plots, two for cases with stiction (plot A and B) and two for cases without stiction (plot C and D). The first graph for each case (plot A and C) shows stiction index according to each method as a function of the number of points per cycle and noise variance, where the displayed value is the average of the stiction index for different phases. The second (plot B and D) shows the accuracy based on the thresholds for each method, shown in Table 1 and taken from the original works.

Method Cross-correlat. Yamashita Curve-fitting Peak slope Zone Segmentat.

Stiction Undetermined > 0.667 ≥ 0.072 and ≤ 0.667 > 0.25 ≥ 0.6 ≥ 0.5 ≥ 0.4

not defined > 0.4 and < 0.6 > 0.3 and < 0.5 > 0.2 and < 0.4

No-Stict. < 0.072 ≤ 0.25 ≤ 0.4 ≤ 0.3 ≤ 0.2

This work aims to evaluate only the effect of sampling period in stiction detection methods and not the effectiveness of each method on stiction detection. Thus, the analyzes were conducted using standard signals according to each paper as segmentation and peaks identification are well as halfperformed for the signal before adding noise to avoid the use of data pre-processing. This procedure is valid since the zeros position (necessary for half-cycles segmentation) and the peaks position are not or slightly displaced after the noise addition, mainly for signals with low sampling, like those analyzed. 3.1. Cross-correlation method analysis For the cross-correlations method, two stiction indices are evaluated: the first based on the lag and the second based on the magnitude of the correlation function. Fig. 4(A) only shows the average for the second index, while Fig 4(B) is the accuracy using both indexes. The analysis for the signal without stiction resulted in 100% correct detection, so it is not displayed. By Fig. 4, it is seen that the stiction index as well as the detection accuracy are strongly influenced by the sampling period, to signals below 14 data per cycle, the accuracy is less than 90%, therefore, this kind of signals must be avoided by analyses using this method. Another important feature of the method, but not related to sampling, is the low

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noise influence on the detection index. This result was expected since the calculation of the cross-correlation function damps the noise effect.

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3.3. Curve fitting method analysis Fig. 6 shows the contour plots for the analysis using curve fitting method. As seen in Fig 6(C) (index for signal without stiction presence), low sampling increases the stiction index (i.e., makes the signal closer to triangular shape, or index above 0.5), while for signals heavily affected by noise, the index tends to approach a random signal (index close to 0.5). Finally, for signals without the presence of noise, the minimum sampling for curve fitting method to ensure at least 90% accuracy is 7 data points per cycle. This value is highly limited by the cases where stiction is not the cause of oscillation, what means that sinusoidal signal is more affected by sampling period and noise variance.

Fig. 4 – Evaluation of the effect of sampling periods on cross-correlation method: (A) stiction index and (B) accuracy for oscillation caused by stiction presence. 3.2. Yamashita method analysis Fig. 5 shows the result for Yamashita method, and, as seen, the stiction detection is not efficient for sampling below 5 data per cycle. For low sampling, typical MV(OP) behavior for stiction presence is not observed, thus the direction of movement found by the method tends to be random, making the stiction index lower than expected (low sampling in Fig 5.A). Thus, at least 5 data per cycle are required for stiction detection by Yamashita method.

Fig. 6 – Evaluation of the effect of sampling periods on curve-fitting method: (A) stiction index and (B) accuracy for oscillation caused by stiction presence; (C) stiction index and (D) accuracy for oscillation caused by other problem. 3.4. Peak slope method analysis

Fig. 5 – Evaluation of the effect of sampling periods on Yamashita method: (A) stiction index and (B) accuracy for oscillation caused by stiction presence; (C) stiction index and (D) accuracy for oscillation caused by other problem.

The peak slope method only evaluates the region located close to the peaks and valleys (evaluation region), thus signals with low sample period cannot be applied, since the evaluation region does not have enough points to evaluate the slope. According to Fig. 7, the application must be limited to signals with at least 12 data per cycle to ensure the accuracy of 90% in the case without the presence of noise. Fig. 7 also shows the strong effect of noise on stiction index. This should be corrected by the use of filters, as shown in the original paper. 3.5. Zone segmentation method analysis Unlike the previous technique, the zone segmentation method can be applied to signals with lower sampling period, but as seen in Fig. 8, detection is efficient only for signals with at least 15 data per cycle for cases where there is no presence of noise. The presence of noise strongly influences the method,

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especially if the cause of the oscillation is not stiction thus the use of filter is required as described in the original paper.

high values do not remove completely the noise from the signal, low values smooth excessively the signal, changing the waveform and affecting the stiction detection. According Dambros et al. (2016), the cutoff frequency for the removal of the noise excess for the curve fitting, peak slope, and zone segmentation methods are respectively equal to 8, 14 and 8 times the signal oscillation frequency. For low sampling signals, values of this magnitude are not possible because the frequency would be located outside the spectrum range (cutoff frequency greater than 1 for the normalized frequency between 0 and 1), thus removing the noise by low-pass filter is not possible. Other types of filters can be used, but they may excessively smooth the signal, especially, again, in the case of low sampling signals. 4. INDUSTRIAL CASE STUDIES

Fig. 7 – Evaluation of the effect of sampling periods on peak slope method: (A) stiction index and (B) accuracy for oscillation caused by stiction presence; (C) stiction index and (D) accuracy for oscillation caused by other problem.

The methods have been applied in industrial data to corroborate the importance of sampling periods. Three signals with unknown cause of oscillation from flow loops of a refinery were used. Fig. 9 shows three fragments, each one with 100 points and, as seen, the signals present respectively 4.5, 6 and 8 data per cycle respectively.

Fig. 9 – Industrial data with respectively 4.5, 6 and 8 data per cycle. From the analysis carried out in Section 3, the use of most the selected detection methods, especially those based on the shape of OP signal, must be discarded. Even though, Table 2 shows the results for the five selected methods for the three signals.

Fig. 8 – Evaluation of the effect of sampling periods on zone segmentation method: (A) stiction index and (B) accuracy for oscillation caused by stiction presence; (C) stiction index and (D) accuracy for oscillation caused by other problem.

Table 2. Stiction detection for industrial data with low sampling, where bolded values are results that obey the restriction from Section 3, “Un” indicates the uncertain presence of stiction and “NA” indicates that the method is not applicable to the signal. Method

3.6. Brief discussion about filtering signals with low sampling Through the previous analyzes, it was observed that the three methods based on OP waveform (i.e., curve fitting, peak slope, and zone segmentation methods) require the use of filters for stiction detection to improve their performance. The removal of noise excess is usually done using low-pass filter, where the cutoff frequency must be chosen carefully:

Cross-correlation Yamashita Curve-fitting Peak slope Zone Segmentation

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Stiction Index F1 0.11 0.22 0.91 NA 0.02

F2 F3 0.16 0.12

Stiction Presence

F1 Un. 0.45 0.26 No 0.64 0.94 Yes NA NA Un. 0.75 0.73 No

F2 Un.

F3 Un.

Yes Yes Un. Yes

Yes Yes Un. Yes

Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 Jônathan W.V. Dambros et al. / IFAC PapersOnLine 50-1 (2017) 2848–2853

As seen in Table 2, the analysis was restricted to a small number of signals and methods. For signal F1, no detection methodology can be applied efficiently since the sampling is less than 5 data per cycle. Also from Table 2, Yamashita method is the only method reliable to be applied to at least two signals, despite this, the zone segmentation method got the same results. For cross-correlation and peaks slope methods the cause of oscillation is uncertain for all the evaluated signals. 5. CONCLUSIONS In this work, the effect of the sampling period in stiction detection methods was analyzed. For the analysis, five detection methods were selected: cross-correlations, Yamashita, curve fitting, peak slope and zone segmentation. The methods were applied to typical signals from fast dynamic processes for the presence and absence of stiction (i.e., sinusoidal, triangular, and square signals). The fast dynamics was considered since in this case the low sampling effect is more relevant. From the analysis, it is concluded that Yamashita method presents the best results for low sampling signals, where the stiction identification was efficient for signals with at least 5 data per cycle. Also, for the sampling range used in the analysis, the index for Yamashita method was slightly influenced by the noise, which should not be true for higher sampling signals. For the cross-correlation method, there is a large variance in the stiction index with the sampling change, even for larger values, but it is possible to obtain good performance for signals with at least 14 data per cycle. For the three methods based on the signal waveform (i.e., curve fitting, peak slope, and zone segmentation methods) the required number of points per cycle is relatively higher. The use of filters is required for signals with strong presence of noise, but, at the same time, its application is limited for low sampling signals. Note that the signals used for the previous analysis are perfectly sinusoidal, triangular and square. The results for the application to signals generated from closed loop simulations could be even worse, since the standard waveform may suffer deviations. For industrial data, fast dynamic processes combined with low sampling lead to signals with few data per cycle, so, despite the clear oscillation in the signal, the cause often cannot be identified. The application of the methods can be made, but many of the results cannot be considered accurate.

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the adjustment of existing one should be considered to overcome this problem. REFERENCES Brásio, A. S. R.; Romanenko, A.; Fernandes, N. C. P. (2014). Modelling, Detection and Quantification, and Compensation of Stiction in Control Loops: The State of the Art. Industrial & Engineering Chemistry Research, v. 53, n. 39, p. 15020-15040. Choudhury, M. A. A. S.; Shah, S.L.; Thornhill N.F.; Shook , D. S. (2006). Automatic detection and quantification of stiction in control valves. Control Engineering Practice, v. 14, n. 12, p. 1395-1412. Dambros, J. W. V.; Farenzena, M.; Trierweiler, J. O. (2016). Data-Based Method to Diagnose Valve Stiction with Variable Reference Signal. Industrial & Engineering Chemistry Research, v. 55, n. 39, p. 10316-10327. Farenzena, M.; Trierweiler, J. O. (2012). Valve Backlash and Stiction Detection in Integrating Processes. Advanced Control of Chemical Processes. 8: 320-324 p. He, Q. P.; Wang, J.; Pottmann, M.; Qin S. J. (2007). A curve fitting method for detecting valve stiction in oscillating control loops. Industrial and Engineering Chemistry Research, v. 46, n. 13, p. 4549-4560. Horch, A. (1999). A simple method for detection of stiction in control valves. Control Engineering Practice, v. 7, n. 10, p. 1221-1231. Kano.M., Maruta.H., Kugemoto.H., Shimizu.K. (2004). Practical model and Detection algorithm for Valve Stiction, Proceedings of IFAC DYCOPS, Cambridge, USA. Paulonis, M. A.; Cox, J. W. (2003). A practical approach for large-scale controller performance assessment, diagnosis, and improvement. Journal of Process Control, v. 13, n. 2, p. 155-168. Rossi, M., Scali, C. (2005). A comparison of techniques for automatic detection of stiction: simulation and application to industrial data. Journal of Process Control, v. 15, n. 5, p. 505-514. Scali, C.; Ghelardoni, C. (2008). An improved qualitative shape analysis technique for automatic detection of valve stiction in flow control loops. Control Engineering Practice, v. 16, n. 12, p. 1501-1508. Singhal, A.; Salsbury, T. I. (2005). A simple method for detecting valve stiction in oscillating control loops. Journal of Process Control, v. 15, n. 4, p. 371-382. Yamashita, Y. (2006). An automatic method for detection of valve stiction in process control loops. Control Engineering Practice, v. 14, n. 5, p. 503-510.

Finally, the main proposed of this paper is to show the lack of techniques for stiction detection in signals with low sampling. Even with high number of detection methods, the identification is restricted to signals with, at least, 5 data per cycle. Consequently, the cause of oscillation in some industrial loops is not possible to be diagnosed in an automatic and non-intrusive way. Therefore, a new method or

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