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Leak detection for long transportation pipeline using a state coupling analysis of pump units
Journal of Loss Prevention in the Process Industries 26 (2013) 586e593
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Journal of Loss Prevention in the Process Industries journal homepage: www.elsevier.com/locate/jlp
Leak detection for long transportation pipeline using a state coupling analysis of pump units Wei Liang*, Jian Kang, Laibin Zhang College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 August 2012 Received in revised form 24 November 2012 Accepted 3 December 2012
Leak detection for long transportation pipeline with a large economic and environmental impact has been an area of intensive research for more than five decades. This paper presents a novel pipeline leak detection scheme based on a state coupling analysis (SCA). Instead of monitoring the pipeline and pump units separately, SCA introduces a new detecting method of analyzing data in a coupling running condition. A novel capture method for abnormal pressure based on logical reasoning algorithm is proposed. Hamming approach degree arithmetic is applied to calculate the matching mode identifying the state of units. SCA is used to reduce the rate of false alarm and detect the leak with a high detecting sensitivity for long transportation pipeline. An on-line software system based on SCA is utilized to achieve superior accuracy and implementation. An industrial case study for coupling system pipeline leak detection is used as an example to validate the effectiveness of the proposed method. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Leak detection Lone transportation pipeline State coupling analysis Pump units state model Abnormal pressure capture
1. Introduction Pipeline leak into the environment is a major problem with large economic and environmental impacts. When a pipeline leak is large or undiscovered in time, substantial volumes of petroleum can leak into the soil, which can lead to a plenty of unexpected downtime and correspondingly high maintenance cost. Numerous methods have been developed with the goal of performing an accurate identification of the leak condition. It is generally accepted that a practical implementation of a detecting method will rely on a consideration of several aspects, such as miss alarm, false alarm, detecting sensitivity and so on. A timely evaluation and response to a leak, allows proper management of the consequences and an effective risk remedy, which becomes a necessity to analyze the performance of the entire transportation system and trigger an alarm before serious failure happens. For most of the pipelines are arranged outside the oil stations, that is, the real-time monitoring of pipelines cannot be obtained simultaneously, which would be inconvenient for accidents such as man-made drilling oil stolen. Negative pressure wave (NPW) based method for leak detection and location has been implemented in most production systems, which mainly carried out for individual pipelines. The problem is that there is lack of capabilities to
* Corresponding author. Tel.: þ86 10 89733326. E-mail addresses: [email protected], [email protected] (W. Liang). 0950-4230/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jlp.2012.12.007
comprehensively analyze the obtained information in order to guide the practitioners to make right decisions before the failure happens. Since many normal operations apart from oil spill events, such as the upstream pump-stopping and the declination of oil temperature may also cause abnormal pressure fluctuations which are similar to leak signals. Various techniques, including NPW have been investigated to address more strength on pattern recognitions for wave pressure in different conditions. Actually, due to the complex fluctuation in the pipeline, the effectiveness of algorithm recognition often cannot fully meet practical requirements which led to high rate of false alarms. Given this condition, measures of reducing the systematic sensitivity carried out. But it should be pointed out that most of these will result in declining capabilities to detect small leak. Present methods for the leak detection range from manual inspection using trained dogs to advance satellite imaging (Santos, Perdicoúlis, & Jank, 2011). They can be classified as acoustic monitoring, optical monitoring, gas sampling, soil monitoring, flow monitoring, model-based methods, etc. A diagnosis model was established (Shetty, 2006) which mixes the evaluation of condition monitoring, failure prediction and performance degradation, and the model considers the influence of installation and operation factors on mechanical dynamics. A kind of monitoring method for operation states of a critical sub-system was developed to detect the potential dangers (Yuan, Zhao, & Qu, 2001). Due to its neglecting of coupling interference that may cause by other sub-systems. So this
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method will provoke a high probability of misdiagnosis. A considerable drawback is the inadequate ability of operation self-adaptive in on-line fault diagnosis system, in which general technical workers are difficult to master the knowledge, and the data depends on the real-time collection as well as off-line analysis by experts (Chen, Pan, Yun, Wang, & Sun, 2008), which is also called on-line trend analysis system (Qiu, Tang, Zhuang, & Yang, 2008). In these models, results are from various sophisticated algorithm for fault diagnostic. Accurate alarms were not obtained since short of ability for self-organization (Zhang, Zhong, & Dai, 2004). These methods cannot be considered completely reliable, which is mainly due to instantaneous conditions. To find an effective way to detect the incipient leak (Gimenez-Guzman & Martinez-Bauset, 2007; Samejima, Katagiri, Doya, & Kawato, 2006), reinforcement learning as an autonomous learning algorithm was applied to nonlinear predictive. A new hierarchical model to forecaster financial trends was proposed by Liu (Liu & Nagao, 2006). In addition, the RL algorithm is widely used in control field (Kaelbling, Littman, & Moore, 1996; Kuremoto, Obayashi, & Kobayashi, 2005; Liu, Ng, & Quek, 2007) due to its advantages in the process that does not need a priori knowledge of self-learning. RL algorithm can acquire experiences and select actions through the interaction with environment. However, it has not been well taken into account in algorithms of fault diagnosis. In previous analysis of the fluid dynamics within the pipeline, and it is acknowledged that tanks, pumps and pipelines comprise a relative closed environment, in which the pump units play as the energy provider and pipelines play as the energy transferring ways. The change of each party sees to create influence on the other. If taking into account of one kind of equipment to implement leak detection without considering the influence of the other, the diagnosis result will be inaccurate easily. Therefore, a more reliable method considering the coupling relationship among associated sub-systems for detecting and locating pipeline leak is currently desirable. A novel leak detection method for long transportation pipeline based on coupling state of pump units is proposed (SCA), which analyzes the system behaviors and abnormal signs by synthesizing the coupling impact between pipelines and pump units. The paper is organized as follows: Section 2 introduces the proposed coupling rules and SCA method for long transportation leak detecting. Section 3 illustrates an industrial case to validate the effectiveness of the proposed method, and the newly developed software systembased SCA is presented, with an emphasis on the coupling analysis technique. Section 4 presents a discussion and future research. Finally, the paper concludes with a conclusion in Section 5.
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2. Leak detection based SCA 2.1. A capture technology of abnormal pipeline pressure based on logical reasoning algorithm In a leak detection system, the performance indexes are usually include sensitivity of leak detection, performance of antiinterference, response time and location accuracy, in which the sensitivity of leak detection and performance of anti-interference respectively represent the minimum detectable capability and the false alarm rate of the system. In the actual process of petroleum storage and transportation, where not only pipeline leak will lead to negative pressure wave but also some normal operations in the station as well, such as a pump switch off from the upstream state, lockout of the valve and a diversion in the state. As evident, the sensitivity of leak detection is inversely proportional to the false alarm rate of the system. These requirements have inspirited the development of a more reasonable and realizable compromise indexes, which comprehensively considers both the actual demand and the necessary to solve the contradiction. A small leak occurs occasionally as part of the aging process of the pipeline, for which more factors should be taken into consideration for environment protection, such as local corrosion, thirdparty interference (Hu, Zhang, & Liang, 2011) and collision by an external force. The traditional pipeline leak detection based on NPV can be divided into two stages, which are abnormal pipeline pressure capturing and leak pattern recognizing for abnormal data. A general scheme of NPV is illustrated in Fig. 1. Real-time ability of the operation system will be affected greatly by the complexity of the leak identification algorithm and the large amount of system resource usage. In normal condition, the function of leak identification can only be started when abnormal pipeline pressure was captured. In order to increase the sensitivity of leak detection system, it is necessary to improve the capture ability especially for abnormal pipeline pressure. The proposed new testing method is called negative pressure wave method based on logical reasoning (NPVLR). The purpose of NPVLR is to reduce computation load and improve on-line performance. In general, NPVLR includes the following four steps: (1) Noise reduction: the real-time pressure data is processed with the method of threshold value judgment to reduce noises. (2) Extract data characteristic indexes (such as rising edge numbers, declining edge numbers and deviation values between the first point and final point and so on).
Fig. 1. Flowchart of the conventional pipeline leak detection using the negative pressure.
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(3) Capture signs of abnormal pressure fluctuations based NPVLR. (4) In one verifying test, let the system capture abnormal pressure fluctuations in conditions of pump switch off, pipeline activity (during this period, the requirements of the operation include adjusting the technological process to prevent the crude oil solidification due to long-time unused pipelines), declination of crude oil temperature and drilled holes. The algorithm flowchart for capturing abnormal pressure based on NPVLR is shown in Fig. 2. For the study, we consider the pipelines with the stable characteristic. NPVLR is assumed to have a powerful capturing ability of abnormal pipeline pressure during the different working condition. Data of pressure fluctuation is obtained in the four conditions. They are pump switch off, pipeline activity, declination of crude oil temperature and drilled holes. The curves of these four kinds of pressure fluctuation can be plotted. Fig. 3 describes the pressure curves following by the noise reduction. In the module implementation process of capturing abnormal signs, the rational analysis is actually only focused on the data within the present “rectangular window” (the time length is 5 min) that will move backward every 15 s. When the present “rectangular window” reaches the appointed positions shown in Fig. 3, the judgment result of “abnormal pressure” will be obtained. Being concerned with the location of pressure inflection point, the response time of capturing abnormal pressure will be calculated. The fluctuation characteristic changes in companies of different pipelines under normal transport condition. Consequently, threshold value schemes should be developed in the process of logic inference program. A several kinds of threshold value schemes are shown in Table 1. The statistical characteristic indexes of noisereduced data segments and the result of abnormal pressure capture are shown in Table 2. According to Table 2, the above four fluctuation sings of abnormal pressure with its response time in 2 min are all captured successfully, of which the last three amplitudes of pressure fluctuation are small, and that manifest the effectiveness of abnormal pressure capture based on logical reasoning method. Because of
Fig. 3. The pipeline pressure after noise reduction. Observed red rectangular windows for each work states will move back every 15 s for searching abnormal signs. The length of rectangular window is 7 min. An abnormal pressure occurs when the rectangular window moved to the position as shown above (indicated by the red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the advantage of the small computation, this method can be used for on-line analysis. 2.2. Model building for real-time pump units state by the Hamming approach degree analysis As the complexity of pump units, its operation state can be described with more than one method. The wave shape coefficient, a dimensionless physical quantity used to describe the relationship between the root mean square and the absolute average value in single piece of data segment is shown as Eq. (1), from which the value of wave shape demonstrates little relationship with the amplitude value of the data but depends on the “shape” of the data greatly. Therefore, a wave shape combination of multi-sensitivity
Fig. 2. Flowchart of capturing abnormal pressure using logical reasoning algorithm.
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by using maximum fuzzy approximation, the Hamming closeness degree is calculated by Eq. (2)
Table 1 Threshold value schemes of the logic inference program. Scheme
Td
Tu
Tt
A (out-station 1) B (out-station 2) C (in-station 1) D (in-station 2)
3 4 4 5
2 1 1 1
0.02 0.02 0.03 0.03
MPa MPa MPa MPa
Ta
Cu
Ct
3 4 5 6
2 2 3 3
0.02 MPa 0.02 MPa 0.015 MPa 0.012 MPa
from single unit is used to establish the wave spectrums of pump unit system, where the sign fluctuation state is measured by the wave shape coefficient.
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n n u1 X 2 1 X Sf ¼ t xi jx j n i¼1 n i¼1 i
(1)
where Sf stands for the wave shape coefficient and xi is the data within the segment. The process of model building and its usage are as follows: (1) Sensitive parameter selection After the analysis of which parameters having great changes with the unit operating state, signal processing and feature extraction algorithms are used to further analyze the significant influence on the pipeline operation state caused by the changes of unit parameters. Within the scope of the acquisition data, 12 sensitivity parameters that can reflect the state of unit operation are selected, which are motor waist vibrate, motor edge vibrate, pump waist vibrate, pump-edge vibrate, pump inlet pressure, pump outlet pressure, motor electric current, motor relative temperature, pump shell temperature, pump operation state, portal tract pressure and portal tract temperature. (2) State standard library of units Common unit states include but not limited to: steady running, pump switch on, pump switch off, load alternating and pump jump off. These typical status parameters are respectively calculated. By using Eq. (1), the wave characteristic for each parameter in five different running state is listed in Table 3. According to the sequence, the waveform indexes for parameters under each condition form a vector defined as the waveform spectrums of the condition. The average of same type wave spectrums coming from multi-groups was used to form a standard wave spectrum for each unit state. (3) State recognition of sample units An obvious problem still remains that is how to choose appropriate classification and identification for sample wave spectrums. Hamming closeness degree identifies the extracted wave spectrums Table 2 Statistical characteristic indexes of noised-reduced data and the capture results of pressure abnormal. Serial number
Drop Rise edge Difference edge number value number
Threshold Response Capture schemes time result
1 2 3 4
27 30 20 25
C C D D
0 1 0 0
0.3186 0.0311 0.0298 0.0359
MPa MPa MPa MPa
589
0.5 min 2 min 2 min 1.5 min
Abnormal Abnormal Abnormal Abnormal
NðA; BÞ ¼ 1
n 1X jAðui Þ Bðui Þj n i¼1
(2)
where n is the number of standard states. A and B represent two kinds of fuzzy sets respectively. In this case, N (A,B) is used as a matching mode to choose the maximum value for testing samples, which is described in the following section. Each state parameters of the testing model can be used to identification and classification of unit states. By using Eq. (1) to calculate the testing state, the Hamming distance in the five types of standard state are respectively 0.7239, 0.7053, 0.9847,0.8341and 0.8463. The maximum value is selected as the matching mode. Therefore, through the calculation, it is belonging to the third state that is known as pump switch off, which conforms to the actual situation. The comparison of wave spectrums between the testing state and the standard state of pump switch off operation is shown in Fig. 4. The field application manifests that the identification based maximum fuzzy approximation method obtained higher accuracy rate as well as faster calculating speed, which can realize real-time identification without a training model. 2.3. The verification of coupling rules between pump units and pipelines Considering the interactions among pump operation state, pump external hydraulic parameters and pump-edge hydraulic parameters, theoretically, there exists strong coupling relationship in the operating state between pump units and pipelines. An actual research will be showed, which goal is to demonstrate that there exist coupling rules between pump unit and pipelines. According to the different transportation methods of crude oil, the pipeline can be generally divided into two types: one is the gathering pipelines within oilegas field, and the other is long transportation pipelines. A simple system of long transportation pipeline is shown in Fig. 5, where each oil transportation station is divided into three segments according to their location. The origin segment is responsible for receiving crude oil and transporting them to the next station after pressurizing or heating. Then, the middle segment is responsible for continued pressurizing or heating. In the end, the final segment is responsible for receiving and distributing oil. From the overall situation, the closed hydraulic system is indeed composed of tank, oil transfer pump and pipelines. Taking one origin segment for example, Fig. 6 shows the variation relationship between the pump unit electric current and the pump outlet pressure at the initial station, and the system is just in the stage of pump switch on. As we can see in Fig. 6, the pump outlet pressure is increasing rapidly (from stage A to stage D) associated with the jumping of motor electric current. During the process of pump switch on, the hydraulic changes in the pipelines are extremely complex. Positive pressure or negative pressure hydraulic impact occurs frequently (such as in section B and section C), which will influence the rotate speed of pump units as well as the magnitude of motor electric current. The pressure variation between pipeline’s upstream and downstream is shown in Fig. 7, when “oil filling” operation was carried out in the station (some crude oil from the main pipelines was released to the fuel tanks), which directly leads to a transient pressure reduction of the outlet pressure, and then the pressure got back to normal within about 5 min. As the long distance between stations, the reduction and recovery of the pressure also took place
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Table 3 Machine wave characteristics in different running state. Serial number
State parameter
Normal conveying
Starting pump operation
Stopping pump operation
Load fluctuation
Jump pump accident
1 2 3 4 5 6 7 8 9 10 11 12
Motor waist watts vibration Motor end watts vibration Pump waist watts vibration Pump end watts vibration Pump inlet pressure Pump outlet pressure Motor current Motor A phase temperature Pump shell temperature Pump running state Remit pipe pressure Remit pipe temperature
1.2469 1.2250 1.2436 1.2725 1.2361 1.2493 1.4920 1.3056 1.0919 1.0001 1.1121 1.9493
2.1680 2.4180 1.0858 1.0572 1.3109 1.0385 1.0447 1.0475 1.1129 1.2540 1.0472 1.1586
1.0056 1.0049 1.0050 1.0048 1.0874 1.0032 1.0005 1.1522 1.1754 1.4153 1.0456 1.2236
1.2270 1.2490 1.1047 1.1183 1.0267 1.0227 1.0696 1.4025 1.1529 1.0001 1.0369 1.1628
1.3135 1.2574 1.0431 1.0612 1.1586 1.1565 1.0100 1.0624 1.3054 1.5264 1.0184 1.0730
after delaying a period in the downstream station. So the trend of changes in pressure waveforms from upstream and downstream is consistent basically. The relationship variation between pipeline’s outlet pressure and outlet flow is shown in Fig. 8. The pressure fluctuation of outlet is high because of the frequency centrifugal oil pump installed in the upstream station, from which it can be seen that the change between the outlet flow and outlet pressure is fundamentally the same. The above analysis further manifests that there exist close connection and strong coupling relationship between the operating state of pump units and pipelines. 2.4. Determine the flowchart of pipeline leak detection based SCA After establishing the real-time state model for pump unit, the basic ideas of the pipeline leak detection method based coupling analysis on the state of pump units are as follows: The abnormal real-time pressure sings of pipelines should be captured, and then the state recognition for the two edges of operating units will be carried out if the captured signs are abnormal. A pipeline leak will be confirmed when the transportation is in a steady state, otherwise the abnormal pipeline pressure can be canceled, and the logical process for the coupling analysis is shown in Fig. 9. Theoretically, the abnormal pressure condition caused by the changing of unit states could be completely excluded owing to the usage of coupling analysis method. So the false alarm rate of the pipeline fault diagnosis system will be reduced greatly. Meanwhile, it is not necessary to consider too much about high false alarm rate of the system so that the capture sensitivity for abnormal pipeline pressure can be set higher, which improves the monitoring ability for small pipeline leak. It manifests the unique advantages of application prospect on the pipeline leak detection based SCA between pipelines and pump units.
Fig. 4. The wave spectrum comparison of the testing model and the pump switch off.
3. An industrial case study of SCA 3.1. System setup Long transportation pipeline is a complicated system which contains many components such as pipelines, compressor, condenser/evaporator, pump units and others. Considering that the overall state is subjected to change in different working conditions, the monitoring is not a trivial task. In the SCA, the whole test pipeline is set to be 156 km including 5 stations (one segment pipeline for crude oil transmission in Donglin multi-pipeline of SINOPEC).
3.2. Leak detection due to oil stolen At 02:20 on January 23rd, 2011, an abnormal pressure fluctuation of pipeline emerged in Qiaozhuang inlet station and Dongying outlet station successively. The leak detection system detected the changes and depicted a pipeline pressure curve. The upstream outlet pressure decreased by 0.06 MPa, and downstream inlet pressure decreased by 0.09 MPa. The coupling analysis function was carried out to identify the diagnosis. The sequence along which we would like the leak detection to proceed is as follows: (1) SCA function was set up to associate the features between the pump units and pipelines, the pump P204 in Dongying station and pump P203 in Qiaozhuang station are used as examples to validate the proposed novel method described in Section 2. (2) By using Eq. (1), the calculated wave spectrum of parameters (S) in the pump units P204 can be obtained, S ¼ {1.2277, 1.2431, 1.2524, 1.2501, 1.2889, 1.400, 1.3616, 1.2924, 1.1147, 1.000, 1.1431, 2.05}. The comparison results between this wave spectrum and five standard states wave spectrum can be plotted. (3) Hamming distances of the wave spectrums between the testing pump unit and the standard ones are as follows respectively, 0.9525, 0.6114, 0.7123, 0.8034 and 0.7273. From the maximum value, it can be inferred that pump P204 in Dongying station is under normal operating condition. The curves representing the process parameters of pump units in P203 station will be obtained respectively. The same method was also used to calculate S of P203, S ¼ {1.2164, 1.2201, 1.2427, 1.2737, 1.2376, 1.2266, 1.6142, 1.3197, 1.2042, 1.000, 1.1803, 1.7024}, the comparison results of that and five standard state wave spectrums can be plotted. Hamming distances of the wave spectrums between pump P203 station and the standard ones are as follows respectively by using
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Fig. 5. Diagram of the long transportation pipeline system.
Eq. (2), 0.9480, 0.6132, 0.7385, 0.8248 and 0.7519. It can be deduced from the above maximum result that pump P203 station is also under normal operating condition. Since the states for upstream pump station P204 and downstream pump station P203 are both in normal condition, and the two stations are the most relevant to the pressure quantity on both sides of the pipeline. The leak detection system identified that it was not an abnormal caused by the changes of units state but a pipeline leak event existing in the segment between P203 and P204, according to which the system sets out an alarm of pipeline leak. Field engineers confirmed that it was a leak failure, for which all the pipelines are shutdown to avoid the leak consequences. Meanwhile, a recovery party was also sent out to the accident segment for emergency repairs, which affirmed that an oil stolen accident has taken place between P203 station and P204 station on January 23rd. Comparing the pressure drop waveform, the time length of this oil stolen accident is 20 min. Taking the volume of illegal oil tank truck as 5 m3, from which it can be estimated that the rate of leak is 15 m3/h and the total amount of pipeline transmission is 1650 m3/h. So the proportion of this accident is calculated as 0.9%. According to the alarm record, a leak detection alarm was obtained within110 s, which can be incorporated into supervising the state of long transportation pipelines, and the results obtained by the system are satisfactory, considering the real-time involved.
in pump P205 is acquired, S ¼ {1.0065, 1.006, 1.0088, 1.008, 1.0177, 1.0718, 1.004, 1.2269, 1.2192, 1.4991, 1.0569, 1.3115}, and the comparison results of which and the five standard state wave spectrums will be obtained. So Hamming distances of the wave spectrums between pump station P205 and the standard ones will be obtained respectively, 0.7369, 0.6936, 0.9692, 0.8650 and 0.8737. The results showed that P205 station is in the pump switch off. Based on the SCA, the abnormal fluctuation was diagnosed as having been caused by the operation of pump switch off. Only an abnormal pressure suggestion was set out by the leak detection system. Meanwhile, coupling with the information from dispatching center that the conveying task for that week was nearing completion, the pump switch off for the whole pipelines in P205 station was carried out at 14:35 on January 26th, 2011 to reduce the transporting capacity. The proposed approach, has been successfully eliminated the false alarm for the pipeline leak by acting as a real-time monitoring warning system for the presence of potentially dangerous scenarios. 3.4. Validation software analysis system
The methodology described above has been implemented in this case of identifying a false alarm, the pipeline segment between Binzhou station (P205) and Zijiao station (P206) was chosen as the study objects. The system, at about 14:35 on January 26th, 2011, had an abnormal pressure fluctuation, where the upstream outlet pressure reduced by 0.35 MPa and downstream inlet pressure reduced by 0.3 MPa. And the pipeline pressure is provided. The same steps (setting up SCA function, calculating wave spectrum and comparing the Hamming distances) are also used to analyze the obtained pressure signs. The process parameters of pump P205 can be plotted. Hence, the wave spectrum of parameters
The design of the software system named as fault diagnosis and security warning system for pump units associated with pipelines, including data access, business logic and user interface, was based on a data stream processing and long-term on-line operation. Taking into consideration factors such as the real-time capability, reliability and configuration flexibility, asynchronous message trigger mechanism was adopted in the software development process. A small-scale, simplified, field testing has been performed for illustrating the behavior of the analysis system proposed, in a 25 km pipeline section that passes through the two transfer stations. The changes of the pressure curves have been showed and tested with one scenario: a leak due to oil stolen occurs in pipeline segment at about 22:56 on February 1st, 2011. The pressure changing from the upstream to downstream pipelines which are adjacent to this segment are obtained respectively.
Fig. 6. Pump unit electricity and pump outlet pressure.
Fig. 7. Pressure of the pipeline upstream and downstream.
3.3. Eliminating a false alarm
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Fig. 8. Pressure and flow of the pump station outlet.
The abnormal pressure fluctuation from the stations along the pipeline segment was captured by the detection module of the software system. Then an oil stolen accident had been diagnosed by the software system, which led to the abnormal pressure this time. Furthermore, the leak point is 17.3 km from the origin station, and the estimating total spill is 30 m3/h based on the total transmission quantity 1500 m3/h at that time. Through field investigation, it turned out that the location error is 40 m and the rate of false alarm is lower than 5%, and the response time is in 60 s. Since the software implementation from December 2010 to March 2011, the system which has been applied to the pipelines transporting crude oil and product oil of Petro China has 10 accident reports (the relative compositions are provided in Table 3). Comparing with the real data provided by field staff, the system successfully detected all the leaks with a false alarm < 5%, average location error 200 m, minimum detectable quantity 0.9% and average response time 96 s. Furthermore, four pump jump accidents were monitored with the root cause analysis. Because of the previous warning related to the security state of pump units, many potential accidents were avoided. The software can also provide technical supports for security transportation of petroleum pipelines. 4. Discussion and future research SCA has been demonstrated to be an effective tool to identify the leak for long transportation pipelines by monitoring the system
state. The first point that deserves some comments is related to the consideration of low sensitivity of existing leakage detection systems, a novel investigative method by means of inferential analysis to capture the abnormal pressure was offered, which conducts reasoning analysis for statistical characteristic indexes of the pressure data. The designing of simple principle and flexibility approach for setting parameters can be used to small leak detection, which is the long transportation pipeline burden. Using sensitive process parameters of the pump units as basic data to built real-time state model. State identification of testing units can be realized by using Hamming closeness degree. With the experimental research between pump units and pipelines, a strong coupling relationship is found. To attempt to reduce the disturbance in system monitoring, a pipeline leak detection method based SCA is proposed, which was applied to fieldwork. This facilitated much faster data acquisition toward the provided useful information analysis. A software analysis system working on a project for long transportation pipeline monitoring, employing supervisory control and data acquisition based SCA approach is to create a network with a view to (i) monitoring the pipeline pressure state and capturing abnormal signs leading to the accidents, (ii) analyzing the cause factor and its effects. Comparing with the conventional leak detection method of negative pressure wave based pattern recognition, the rate of false alarm has also ready reduced by 5%, meaning that information from the process data has been correctly analyzed. Also the mistake on the identified signs has been reduced greatly. With shortening the average response time for 50%, the least detection quantity has increased to 0.9%. As the software system converges to the actual fault, it has been verified that the proposed method is effective to identify the leak by monitoring signs from pump units and pipelines. In continuing with the SCA tool, it seems worthwhile to make the improvement. The data acquisition range should be extended to handle multiple component faults occurring either simultaneously or at different instants so that more complex fault scenarios could be efficiently diagnosed. Based on the logical reasoning, the proposed SCA method can be utilized to predict the running trend of the long transportation pipeline system for preventing failure before it happens. 5. Conclusion In this contribution, a detection algorithm based on state coupling analysis has been proposed for the leak of long transportation pipeline system. This tool has then been carried out to a field application in order to perform an on-line identification of the running condition of petroleum transfer stations. The interest of coupling analyzed information for enhanced state monitoring has been underlined by capturing abnormal pressure signs provided by the tool-namely logical reasoning method and the calculating space distance tool-namely Hamming approach degree. Considering the convenience for implementation in field, an on-line software system was developed. It also allows for the detection, location, and quantification of the leak affecting the long transportation pipeline. Acknowledgment
Fig. 9. Flowchart of the pipeline leak detection based on the coupling analysis.
The research is supported by National Natural Science Foundation of China (Grant No. 51005247), Beijing Nova Program (Grant No. 2010B068), National Science and Technology Major Project of China (Grant No. 2011ZX05055), National Key Technology Research and Development Program of China (Grant No. 2011BAK06B01).
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Liu, S., & Nagao, T. (2006). HXCS and its application to financial time series forecasting. IEEE Transactions on Electrical and Electronic Engineering, 1(4), 417e425. Liu, F., Ng, G. S., & Quek, C. (2007). RLDDE: a novel reinforcement learning-based dimension and delay estimator for neural networks in time series prediction. Neurocomputing, 70(7e9), 1331e1341. Samejima, K., Katagiri, K., Doya, K., & Kawato, M. (2006). Multiple model-based reinforcement learning for nonlinear control. Electronics and Communications in Japan Part III e Fundamental Electronic Science, 89(9), 54e69. Santos, P. L., Perdicoúlis, T. P. A., & Jank, G. (2011). Leakage detection and location in gas pipelines through an LPV identification approach. Communications in Nonlinear Science and Numerical Simulation, 25, 467e477. Shetty, P., (2006). A hybrid prognostic model formulation system identification and health estimation of auxiliary power units. In IEEE aerospace conference. Yuan, X. H., Zhao, H. S., & Qu, L. S. (2001). On the application of rough set theory in mechanical fault diagnosis. Journal of Xi’an Jiaotong University, 35(9), 954e957, (in Chinese). Zhang, J. X., Zhong, Q. H., & Dai, Y. P. (2004). Fault diagnosis of rotating machinery using RBF and fuzzy neural network. Acta Simulata Systematica Sinica, 16(3), 560e563, (in Chinese).
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Journal of Loss Prevention in the Process Industries journal homepage: www.elsevier.com/locate/jlp
Leak detection for long transportation pipeline using a state coupling analysis of pump units Wei Liang*, Jian Kang, Laibin Zhang College of Mechanical and Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 August 2012 Received in revised form 24 November 2012 Accepted 3 December 2012
Leak detection for long transportation pipeline with a large economic and environmental impact has been an area of intensive research for more than five decades. This paper presents a novel pipeline leak detection scheme based on a state coupling analysis (SCA). Instead of monitoring the pipeline and pump units separately, SCA introduces a new detecting method of analyzing data in a coupling running condition. A novel capture method for abnormal pressure based on logical reasoning algorithm is proposed. Hamming approach degree arithmetic is applied to calculate the matching mode identifying the state of units. SCA is used to reduce the rate of false alarm and detect the leak with a high detecting sensitivity for long transportation pipeline. An on-line software system based on SCA is utilized to achieve superior accuracy and implementation. An industrial case study for coupling system pipeline leak detection is used as an example to validate the effectiveness of the proposed method. Ó 2013 Elsevier Ltd. All rights reserved.
Keywords: Leak detection Lone transportation pipeline State coupling analysis Pump units state model Abnormal pressure capture
1. Introduction Pipeline leak into the environment is a major problem with large economic and environmental impacts. When a pipeline leak is large or undiscovered in time, substantial volumes of petroleum can leak into the soil, which can lead to a plenty of unexpected downtime and correspondingly high maintenance cost. Numerous methods have been developed with the goal of performing an accurate identification of the leak condition. It is generally accepted that a practical implementation of a detecting method will rely on a consideration of several aspects, such as miss alarm, false alarm, detecting sensitivity and so on. A timely evaluation and response to a leak, allows proper management of the consequences and an effective risk remedy, which becomes a necessity to analyze the performance of the entire transportation system and trigger an alarm before serious failure happens. For most of the pipelines are arranged outside the oil stations, that is, the real-time monitoring of pipelines cannot be obtained simultaneously, which would be inconvenient for accidents such as man-made drilling oil stolen. Negative pressure wave (NPW) based method for leak detection and location has been implemented in most production systems, which mainly carried out for individual pipelines. The problem is that there is lack of capabilities to
* Corresponding author. Tel.: þ86 10 89733326. E-mail addresses: [email protected], [email protected] (W. Liang). 0950-4230/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jlp.2012.12.007
comprehensively analyze the obtained information in order to guide the practitioners to make right decisions before the failure happens. Since many normal operations apart from oil spill events, such as the upstream pump-stopping and the declination of oil temperature may also cause abnormal pressure fluctuations which are similar to leak signals. Various techniques, including NPW have been investigated to address more strength on pattern recognitions for wave pressure in different conditions. Actually, due to the complex fluctuation in the pipeline, the effectiveness of algorithm recognition often cannot fully meet practical requirements which led to high rate of false alarms. Given this condition, measures of reducing the systematic sensitivity carried out. But it should be pointed out that most of these will result in declining capabilities to detect small leak. Present methods for the leak detection range from manual inspection using trained dogs to advance satellite imaging (Santos, Perdicoúlis, & Jank, 2011). They can be classified as acoustic monitoring, optical monitoring, gas sampling, soil monitoring, flow monitoring, model-based methods, etc. A diagnosis model was established (Shetty, 2006) which mixes the evaluation of condition monitoring, failure prediction and performance degradation, and the model considers the influence of installation and operation factors on mechanical dynamics. A kind of monitoring method for operation states of a critical sub-system was developed to detect the potential dangers (Yuan, Zhao, & Qu, 2001). Due to its neglecting of coupling interference that may cause by other sub-systems. So this
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method will provoke a high probability of misdiagnosis. A considerable drawback is the inadequate ability of operation self-adaptive in on-line fault diagnosis system, in which general technical workers are difficult to master the knowledge, and the data depends on the real-time collection as well as off-line analysis by experts (Chen, Pan, Yun, Wang, & Sun, 2008), which is also called on-line trend analysis system (Qiu, Tang, Zhuang, & Yang, 2008). In these models, results are from various sophisticated algorithm for fault diagnostic. Accurate alarms were not obtained since short of ability for self-organization (Zhang, Zhong, & Dai, 2004). These methods cannot be considered completely reliable, which is mainly due to instantaneous conditions. To find an effective way to detect the incipient leak (Gimenez-Guzman & Martinez-Bauset, 2007; Samejima, Katagiri, Doya, & Kawato, 2006), reinforcement learning as an autonomous learning algorithm was applied to nonlinear predictive. A new hierarchical model to forecaster financial trends was proposed by Liu (Liu & Nagao, 2006). In addition, the RL algorithm is widely used in control field (Kaelbling, Littman, & Moore, 1996; Kuremoto, Obayashi, & Kobayashi, 2005; Liu, Ng, & Quek, 2007) due to its advantages in the process that does not need a priori knowledge of self-learning. RL algorithm can acquire experiences and select actions through the interaction with environment. However, it has not been well taken into account in algorithms of fault diagnosis. In previous analysis of the fluid dynamics within the pipeline, and it is acknowledged that tanks, pumps and pipelines comprise a relative closed environment, in which the pump units play as the energy provider and pipelines play as the energy transferring ways. The change of each party sees to create influence on the other. If taking into account of one kind of equipment to implement leak detection without considering the influence of the other, the diagnosis result will be inaccurate easily. Therefore, a more reliable method considering the coupling relationship among associated sub-systems for detecting and locating pipeline leak is currently desirable. A novel leak detection method for long transportation pipeline based on coupling state of pump units is proposed (SCA), which analyzes the system behaviors and abnormal signs by synthesizing the coupling impact between pipelines and pump units. The paper is organized as follows: Section 2 introduces the proposed coupling rules and SCA method for long transportation leak detecting. Section 3 illustrates an industrial case to validate the effectiveness of the proposed method, and the newly developed software systembased SCA is presented, with an emphasis on the coupling analysis technique. Section 4 presents a discussion and future research. Finally, the paper concludes with a conclusion in Section 5.
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2. Leak detection based SCA 2.1. A capture technology of abnormal pipeline pressure based on logical reasoning algorithm In a leak detection system, the performance indexes are usually include sensitivity of leak detection, performance of antiinterference, response time and location accuracy, in which the sensitivity of leak detection and performance of anti-interference respectively represent the minimum detectable capability and the false alarm rate of the system. In the actual process of petroleum storage and transportation, where not only pipeline leak will lead to negative pressure wave but also some normal operations in the station as well, such as a pump switch off from the upstream state, lockout of the valve and a diversion in the state. As evident, the sensitivity of leak detection is inversely proportional to the false alarm rate of the system. These requirements have inspirited the development of a more reasonable and realizable compromise indexes, which comprehensively considers both the actual demand and the necessary to solve the contradiction. A small leak occurs occasionally as part of the aging process of the pipeline, for which more factors should be taken into consideration for environment protection, such as local corrosion, thirdparty interference (Hu, Zhang, & Liang, 2011) and collision by an external force. The traditional pipeline leak detection based on NPV can be divided into two stages, which are abnormal pipeline pressure capturing and leak pattern recognizing for abnormal data. A general scheme of NPV is illustrated in Fig. 1. Real-time ability of the operation system will be affected greatly by the complexity of the leak identification algorithm and the large amount of system resource usage. In normal condition, the function of leak identification can only be started when abnormal pipeline pressure was captured. In order to increase the sensitivity of leak detection system, it is necessary to improve the capture ability especially for abnormal pipeline pressure. The proposed new testing method is called negative pressure wave method based on logical reasoning (NPVLR). The purpose of NPVLR is to reduce computation load and improve on-line performance. In general, NPVLR includes the following four steps: (1) Noise reduction: the real-time pressure data is processed with the method of threshold value judgment to reduce noises. (2) Extract data characteristic indexes (such as rising edge numbers, declining edge numbers and deviation values between the first point and final point and so on).
Fig. 1. Flowchart of the conventional pipeline leak detection using the negative pressure.
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(3) Capture signs of abnormal pressure fluctuations based NPVLR. (4) In one verifying test, let the system capture abnormal pressure fluctuations in conditions of pump switch off, pipeline activity (during this period, the requirements of the operation include adjusting the technological process to prevent the crude oil solidification due to long-time unused pipelines), declination of crude oil temperature and drilled holes. The algorithm flowchart for capturing abnormal pressure based on NPVLR is shown in Fig. 2. For the study, we consider the pipelines with the stable characteristic. NPVLR is assumed to have a powerful capturing ability of abnormal pipeline pressure during the different working condition. Data of pressure fluctuation is obtained in the four conditions. They are pump switch off, pipeline activity, declination of crude oil temperature and drilled holes. The curves of these four kinds of pressure fluctuation can be plotted. Fig. 3 describes the pressure curves following by the noise reduction. In the module implementation process of capturing abnormal signs, the rational analysis is actually only focused on the data within the present “rectangular window” (the time length is 5 min) that will move backward every 15 s. When the present “rectangular window” reaches the appointed positions shown in Fig. 3, the judgment result of “abnormal pressure” will be obtained. Being concerned with the location of pressure inflection point, the response time of capturing abnormal pressure will be calculated. The fluctuation characteristic changes in companies of different pipelines under normal transport condition. Consequently, threshold value schemes should be developed in the process of logic inference program. A several kinds of threshold value schemes are shown in Table 1. The statistical characteristic indexes of noisereduced data segments and the result of abnormal pressure capture are shown in Table 2. According to Table 2, the above four fluctuation sings of abnormal pressure with its response time in 2 min are all captured successfully, of which the last three amplitudes of pressure fluctuation are small, and that manifest the effectiveness of abnormal pressure capture based on logical reasoning method. Because of
Fig. 3. The pipeline pressure after noise reduction. Observed red rectangular windows for each work states will move back every 15 s for searching abnormal signs. The length of rectangular window is 7 min. An abnormal pressure occurs when the rectangular window moved to the position as shown above (indicated by the red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
the advantage of the small computation, this method can be used for on-line analysis. 2.2. Model building for real-time pump units state by the Hamming approach degree analysis As the complexity of pump units, its operation state can be described with more than one method. The wave shape coefficient, a dimensionless physical quantity used to describe the relationship between the root mean square and the absolute average value in single piece of data segment is shown as Eq. (1), from which the value of wave shape demonstrates little relationship with the amplitude value of the data but depends on the “shape” of the data greatly. Therefore, a wave shape combination of multi-sensitivity
Fig. 2. Flowchart of capturing abnormal pressure using logical reasoning algorithm.
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by using maximum fuzzy approximation, the Hamming closeness degree is calculated by Eq. (2)
Table 1 Threshold value schemes of the logic inference program. Scheme
Td
Tu
Tt
A (out-station 1) B (out-station 2) C (in-station 1) D (in-station 2)
3 4 4 5
2 1 1 1
0.02 0.02 0.03 0.03
MPa MPa MPa MPa
Ta
Cu
Ct
3 4 5 6
2 2 3 3
0.02 MPa 0.02 MPa 0.015 MPa 0.012 MPa
from single unit is used to establish the wave spectrums of pump unit system, where the sign fluctuation state is measured by the wave shape coefficient.
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n n u1 X 2 1 X Sf ¼ t xi jx j n i¼1 n i¼1 i
(1)
where Sf stands for the wave shape coefficient and xi is the data within the segment. The process of model building and its usage are as follows: (1) Sensitive parameter selection After the analysis of which parameters having great changes with the unit operating state, signal processing and feature extraction algorithms are used to further analyze the significant influence on the pipeline operation state caused by the changes of unit parameters. Within the scope of the acquisition data, 12 sensitivity parameters that can reflect the state of unit operation are selected, which are motor waist vibrate, motor edge vibrate, pump waist vibrate, pump-edge vibrate, pump inlet pressure, pump outlet pressure, motor electric current, motor relative temperature, pump shell temperature, pump operation state, portal tract pressure and portal tract temperature. (2) State standard library of units Common unit states include but not limited to: steady running, pump switch on, pump switch off, load alternating and pump jump off. These typical status parameters are respectively calculated. By using Eq. (1), the wave characteristic for each parameter in five different running state is listed in Table 3. According to the sequence, the waveform indexes for parameters under each condition form a vector defined as the waveform spectrums of the condition. The average of same type wave spectrums coming from multi-groups was used to form a standard wave spectrum for each unit state. (3) State recognition of sample units An obvious problem still remains that is how to choose appropriate classification and identification for sample wave spectrums. Hamming closeness degree identifies the extracted wave spectrums Table 2 Statistical characteristic indexes of noised-reduced data and the capture results of pressure abnormal. Serial number
Drop Rise edge Difference edge number value number
Threshold Response Capture schemes time result
1 2 3 4
27 30 20 25
C C D D
0 1 0 0
0.3186 0.0311 0.0298 0.0359
MPa MPa MPa MPa
589
0.5 min 2 min 2 min 1.5 min
Abnormal Abnormal Abnormal Abnormal
NðA; BÞ ¼ 1
n 1X jAðui Þ Bðui Þj n i¼1
(2)
where n is the number of standard states. A and B represent two kinds of fuzzy sets respectively. In this case, N (A,B) is used as a matching mode to choose the maximum value for testing samples, which is described in the following section. Each state parameters of the testing model can be used to identification and classification of unit states. By using Eq. (1) to calculate the testing state, the Hamming distance in the five types of standard state are respectively 0.7239, 0.7053, 0.9847,0.8341and 0.8463. The maximum value is selected as the matching mode. Therefore, through the calculation, it is belonging to the third state that is known as pump switch off, which conforms to the actual situation. The comparison of wave spectrums between the testing state and the standard state of pump switch off operation is shown in Fig. 4. The field application manifests that the identification based maximum fuzzy approximation method obtained higher accuracy rate as well as faster calculating speed, which can realize real-time identification without a training model. 2.3. The verification of coupling rules between pump units and pipelines Considering the interactions among pump operation state, pump external hydraulic parameters and pump-edge hydraulic parameters, theoretically, there exists strong coupling relationship in the operating state between pump units and pipelines. An actual research will be showed, which goal is to demonstrate that there exist coupling rules between pump unit and pipelines. According to the different transportation methods of crude oil, the pipeline can be generally divided into two types: one is the gathering pipelines within oilegas field, and the other is long transportation pipelines. A simple system of long transportation pipeline is shown in Fig. 5, where each oil transportation station is divided into three segments according to their location. The origin segment is responsible for receiving crude oil and transporting them to the next station after pressurizing or heating. Then, the middle segment is responsible for continued pressurizing or heating. In the end, the final segment is responsible for receiving and distributing oil. From the overall situation, the closed hydraulic system is indeed composed of tank, oil transfer pump and pipelines. Taking one origin segment for example, Fig. 6 shows the variation relationship between the pump unit electric current and the pump outlet pressure at the initial station, and the system is just in the stage of pump switch on. As we can see in Fig. 6, the pump outlet pressure is increasing rapidly (from stage A to stage D) associated with the jumping of motor electric current. During the process of pump switch on, the hydraulic changes in the pipelines are extremely complex. Positive pressure or negative pressure hydraulic impact occurs frequently (such as in section B and section C), which will influence the rotate speed of pump units as well as the magnitude of motor electric current. The pressure variation between pipeline’s upstream and downstream is shown in Fig. 7, when “oil filling” operation was carried out in the station (some crude oil from the main pipelines was released to the fuel tanks), which directly leads to a transient pressure reduction of the outlet pressure, and then the pressure got back to normal within about 5 min. As the long distance between stations, the reduction and recovery of the pressure also took place
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Table 3 Machine wave characteristics in different running state. Serial number
State parameter
Normal conveying
Starting pump operation
Stopping pump operation
Load fluctuation
Jump pump accident
1 2 3 4 5 6 7 8 9 10 11 12
Motor waist watts vibration Motor end watts vibration Pump waist watts vibration Pump end watts vibration Pump inlet pressure Pump outlet pressure Motor current Motor A phase temperature Pump shell temperature Pump running state Remit pipe pressure Remit pipe temperature
1.2469 1.2250 1.2436 1.2725 1.2361 1.2493 1.4920 1.3056 1.0919 1.0001 1.1121 1.9493
2.1680 2.4180 1.0858 1.0572 1.3109 1.0385 1.0447 1.0475 1.1129 1.2540 1.0472 1.1586
1.0056 1.0049 1.0050 1.0048 1.0874 1.0032 1.0005 1.1522 1.1754 1.4153 1.0456 1.2236
1.2270 1.2490 1.1047 1.1183 1.0267 1.0227 1.0696 1.4025 1.1529 1.0001 1.0369 1.1628
1.3135 1.2574 1.0431 1.0612 1.1586 1.1565 1.0100 1.0624 1.3054 1.5264 1.0184 1.0730
after delaying a period in the downstream station. So the trend of changes in pressure waveforms from upstream and downstream is consistent basically. The relationship variation between pipeline’s outlet pressure and outlet flow is shown in Fig. 8. The pressure fluctuation of outlet is high because of the frequency centrifugal oil pump installed in the upstream station, from which it can be seen that the change between the outlet flow and outlet pressure is fundamentally the same. The above analysis further manifests that there exist close connection and strong coupling relationship between the operating state of pump units and pipelines. 2.4. Determine the flowchart of pipeline leak detection based SCA After establishing the real-time state model for pump unit, the basic ideas of the pipeline leak detection method based coupling analysis on the state of pump units are as follows: The abnormal real-time pressure sings of pipelines should be captured, and then the state recognition for the two edges of operating units will be carried out if the captured signs are abnormal. A pipeline leak will be confirmed when the transportation is in a steady state, otherwise the abnormal pipeline pressure can be canceled, and the logical process for the coupling analysis is shown in Fig. 9. Theoretically, the abnormal pressure condition caused by the changing of unit states could be completely excluded owing to the usage of coupling analysis method. So the false alarm rate of the pipeline fault diagnosis system will be reduced greatly. Meanwhile, it is not necessary to consider too much about high false alarm rate of the system so that the capture sensitivity for abnormal pipeline pressure can be set higher, which improves the monitoring ability for small pipeline leak. It manifests the unique advantages of application prospect on the pipeline leak detection based SCA between pipelines and pump units.
Fig. 4. The wave spectrum comparison of the testing model and the pump switch off.
3. An industrial case study of SCA 3.1. System setup Long transportation pipeline is a complicated system which contains many components such as pipelines, compressor, condenser/evaporator, pump units and others. Considering that the overall state is subjected to change in different working conditions, the monitoring is not a trivial task. In the SCA, the whole test pipeline is set to be 156 km including 5 stations (one segment pipeline for crude oil transmission in Donglin multi-pipeline of SINOPEC).
3.2. Leak detection due to oil stolen At 02:20 on January 23rd, 2011, an abnormal pressure fluctuation of pipeline emerged in Qiaozhuang inlet station and Dongying outlet station successively. The leak detection system detected the changes and depicted a pipeline pressure curve. The upstream outlet pressure decreased by 0.06 MPa, and downstream inlet pressure decreased by 0.09 MPa. The coupling analysis function was carried out to identify the diagnosis. The sequence along which we would like the leak detection to proceed is as follows: (1) SCA function was set up to associate the features between the pump units and pipelines, the pump P204 in Dongying station and pump P203 in Qiaozhuang station are used as examples to validate the proposed novel method described in Section 2. (2) By using Eq. (1), the calculated wave spectrum of parameters (S) in the pump units P204 can be obtained, S ¼ {1.2277, 1.2431, 1.2524, 1.2501, 1.2889, 1.400, 1.3616, 1.2924, 1.1147, 1.000, 1.1431, 2.05}. The comparison results between this wave spectrum and five standard states wave spectrum can be plotted. (3) Hamming distances of the wave spectrums between the testing pump unit and the standard ones are as follows respectively, 0.9525, 0.6114, 0.7123, 0.8034 and 0.7273. From the maximum value, it can be inferred that pump P204 in Dongying station is under normal operating condition. The curves representing the process parameters of pump units in P203 station will be obtained respectively. The same method was also used to calculate S of P203, S ¼ {1.2164, 1.2201, 1.2427, 1.2737, 1.2376, 1.2266, 1.6142, 1.3197, 1.2042, 1.000, 1.1803, 1.7024}, the comparison results of that and five standard state wave spectrums can be plotted. Hamming distances of the wave spectrums between pump P203 station and the standard ones are as follows respectively by using
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Fig. 5. Diagram of the long transportation pipeline system.
Eq. (2), 0.9480, 0.6132, 0.7385, 0.8248 and 0.7519. It can be deduced from the above maximum result that pump P203 station is also under normal operating condition. Since the states for upstream pump station P204 and downstream pump station P203 are both in normal condition, and the two stations are the most relevant to the pressure quantity on both sides of the pipeline. The leak detection system identified that it was not an abnormal caused by the changes of units state but a pipeline leak event existing in the segment between P203 and P204, according to which the system sets out an alarm of pipeline leak. Field engineers confirmed that it was a leak failure, for which all the pipelines are shutdown to avoid the leak consequences. Meanwhile, a recovery party was also sent out to the accident segment for emergency repairs, which affirmed that an oil stolen accident has taken place between P203 station and P204 station on January 23rd. Comparing the pressure drop waveform, the time length of this oil stolen accident is 20 min. Taking the volume of illegal oil tank truck as 5 m3, from which it can be estimated that the rate of leak is 15 m3/h and the total amount of pipeline transmission is 1650 m3/h. So the proportion of this accident is calculated as 0.9%. According to the alarm record, a leak detection alarm was obtained within110 s, which can be incorporated into supervising the state of long transportation pipelines, and the results obtained by the system are satisfactory, considering the real-time involved.
in pump P205 is acquired, S ¼ {1.0065, 1.006, 1.0088, 1.008, 1.0177, 1.0718, 1.004, 1.2269, 1.2192, 1.4991, 1.0569, 1.3115}, and the comparison results of which and the five standard state wave spectrums will be obtained. So Hamming distances of the wave spectrums between pump station P205 and the standard ones will be obtained respectively, 0.7369, 0.6936, 0.9692, 0.8650 and 0.8737. The results showed that P205 station is in the pump switch off. Based on the SCA, the abnormal fluctuation was diagnosed as having been caused by the operation of pump switch off. Only an abnormal pressure suggestion was set out by the leak detection system. Meanwhile, coupling with the information from dispatching center that the conveying task for that week was nearing completion, the pump switch off for the whole pipelines in P205 station was carried out at 14:35 on January 26th, 2011 to reduce the transporting capacity. The proposed approach, has been successfully eliminated the false alarm for the pipeline leak by acting as a real-time monitoring warning system for the presence of potentially dangerous scenarios. 3.4. Validation software analysis system
The methodology described above has been implemented in this case of identifying a false alarm, the pipeline segment between Binzhou station (P205) and Zijiao station (P206) was chosen as the study objects. The system, at about 14:35 on January 26th, 2011, had an abnormal pressure fluctuation, where the upstream outlet pressure reduced by 0.35 MPa and downstream inlet pressure reduced by 0.3 MPa. And the pipeline pressure is provided. The same steps (setting up SCA function, calculating wave spectrum and comparing the Hamming distances) are also used to analyze the obtained pressure signs. The process parameters of pump P205 can be plotted. Hence, the wave spectrum of parameters
The design of the software system named as fault diagnosis and security warning system for pump units associated with pipelines, including data access, business logic and user interface, was based on a data stream processing and long-term on-line operation. Taking into consideration factors such as the real-time capability, reliability and configuration flexibility, asynchronous message trigger mechanism was adopted in the software development process. A small-scale, simplified, field testing has been performed for illustrating the behavior of the analysis system proposed, in a 25 km pipeline section that passes through the two transfer stations. The changes of the pressure curves have been showed and tested with one scenario: a leak due to oil stolen occurs in pipeline segment at about 22:56 on February 1st, 2011. The pressure changing from the upstream to downstream pipelines which are adjacent to this segment are obtained respectively.
Fig. 6. Pump unit electricity and pump outlet pressure.
Fig. 7. Pressure of the pipeline upstream and downstream.
3.3. Eliminating a false alarm
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Fig. 8. Pressure and flow of the pump station outlet.
The abnormal pressure fluctuation from the stations along the pipeline segment was captured by the detection module of the software system. Then an oil stolen accident had been diagnosed by the software system, which led to the abnormal pressure this time. Furthermore, the leak point is 17.3 km from the origin station, and the estimating total spill is 30 m3/h based on the total transmission quantity 1500 m3/h at that time. Through field investigation, it turned out that the location error is 40 m and the rate of false alarm is lower than 5%, and the response time is in 60 s. Since the software implementation from December 2010 to March 2011, the system which has been applied to the pipelines transporting crude oil and product oil of Petro China has 10 accident reports (the relative compositions are provided in Table 3). Comparing with the real data provided by field staff, the system successfully detected all the leaks with a false alarm < 5%, average location error 200 m, minimum detectable quantity 0.9% and average response time 96 s. Furthermore, four pump jump accidents were monitored with the root cause analysis. Because of the previous warning related to the security state of pump units, many potential accidents were avoided. The software can also provide technical supports for security transportation of petroleum pipelines. 4. Discussion and future research SCA has been demonstrated to be an effective tool to identify the leak for long transportation pipelines by monitoring the system
state. The first point that deserves some comments is related to the consideration of low sensitivity of existing leakage detection systems, a novel investigative method by means of inferential analysis to capture the abnormal pressure was offered, which conducts reasoning analysis for statistical characteristic indexes of the pressure data. The designing of simple principle and flexibility approach for setting parameters can be used to small leak detection, which is the long transportation pipeline burden. Using sensitive process parameters of the pump units as basic data to built real-time state model. State identification of testing units can be realized by using Hamming closeness degree. With the experimental research between pump units and pipelines, a strong coupling relationship is found. To attempt to reduce the disturbance in system monitoring, a pipeline leak detection method based SCA is proposed, which was applied to fieldwork. This facilitated much faster data acquisition toward the provided useful information analysis. A software analysis system working on a project for long transportation pipeline monitoring, employing supervisory control and data acquisition based SCA approach is to create a network with a view to (i) monitoring the pipeline pressure state and capturing abnormal signs leading to the accidents, (ii) analyzing the cause factor and its effects. Comparing with the conventional leak detection method of negative pressure wave based pattern recognition, the rate of false alarm has also ready reduced by 5%, meaning that information from the process data has been correctly analyzed. Also the mistake on the identified signs has been reduced greatly. With shortening the average response time for 50%, the least detection quantity has increased to 0.9%. As the software system converges to the actual fault, it has been verified that the proposed method is effective to identify the leak by monitoring signs from pump units and pipelines. In continuing with the SCA tool, it seems worthwhile to make the improvement. The data acquisition range should be extended to handle multiple component faults occurring either simultaneously or at different instants so that more complex fault scenarios could be efficiently diagnosed. Based on the logical reasoning, the proposed SCA method can be utilized to predict the running trend of the long transportation pipeline system for preventing failure before it happens. 5. Conclusion In this contribution, a detection algorithm based on state coupling analysis has been proposed for the leak of long transportation pipeline system. This tool has then been carried out to a field application in order to perform an on-line identification of the running condition of petroleum transfer stations. The interest of coupling analyzed information for enhanced state monitoring has been underlined by capturing abnormal pressure signs provided by the tool-namely logical reasoning method and the calculating space distance tool-namely Hamming approach degree. Considering the convenience for implementation in field, an on-line software system was developed. It also allows for the detection, location, and quantification of the leak affecting the long transportation pipeline. Acknowledgment
Fig. 9. Flowchart of the pipeline leak detection based on the coupling analysis.
The research is supported by National Natural Science Foundation of China (Grant No. 51005247), Beijing Nova Program (Grant No. 2010B068), National Science and Technology Major Project of China (Grant No. 2011ZX05055), National Key Technology Research and Development Program of China (Grant No. 2011BAK06B01).
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