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Comments on the applicability of “An improved weighted recursive PCA algorithm for adaptive fault detection”
Control Engineering Practice xx (xxxx) xxxx–xxxx
Contents lists available at ScienceDirect
Control Engineering Practice journal homepage: www.elsevier.com/locate/conengprac
Comments on the applicability of “An improved weighted recursive PCA algorithm for adaptive fault detection” ⁎
Marcos Quiñones-Grueiroa, , Cristina Verdeb a b
Department of Automation and Computing, Cujae, Havana, Cuba Instituto de Ingeniera, UNAM, Mexico City, México
A R T I C L E I N F O
A BS T RAC T
Keywords: Data-driven fault detection False alarm rate Weighted recursive PCA Fault realization test Computational complexity
This paper discusses some limitations of the weighted recursive PCA algorithm (WARP) proposed by Portnoy, Melendez, Pinzon, and Sanjuan (2016) which is used for fault detection (FD) by arguing that it can reduce false alarms. The motivation of these comments is the lack of a clear criterion in the WARP algorithm to distinguish between process deviations and faults' scenarios, and as a consequence, the applicability of this algorithm is questionable from the FD point of view. Moreover, we address the absence of a formal justification why the computational complexity achieved by using the WARP algorithm is reduced in comparison with methods discussed in the paper.
1. Main discussion
based on expert systems for the FD task (Henley, 1984); however, this does not mean that every operator of an industrial system can decide on-line if a specific hazardous condition is present. Therefore from a safety point of view, the WARP algorithm is unsuitable for application in a real large-scale process. Do not forget that according to Venkatasubramanian, Rengaswamyd, Yin, and Kavuri (2003) 70% of the industrial accidents are provoked by human errors. As a consequence of the above facts, the FDI community suggests that additional process considerations must be included in the adaptive PCA schemes to achieve success from a fault detection point of view. As examples, the procedure presented in Wang, Wang, Wang, and Qian (2013) which considers multi-modes, or the algorithm published in Mina and Verde (2007) which assumes a static relation between some process variables can be applied. Moreover, structural analysis (SA) can be complemented with dynamic principal component analysis (DPCA) to tackle isolability issues of large-scale systems, as it is used by Mina, Verde, Sánchez-Parra, and Ortega (2008) for the specific case of a gas turbine. An important property of the WARP algorithm is the reduction of the computational complexity. One has to clarify, however, how it can be reduced in comparison with the method proposed by Li, Yue, ValleCervantes, and Qin (2000) if the set of equations used for the recursive update of the statistical parameters are similar in both algorithms. The use of the forgetting factor as w = 1 − μ or its complement μ cannot change the results and the computational magnitude order. A formal justification of why the WARP algorithm achieves a reduction in the computational complexity is missing in Section 4 of the paper.
The basic idea behind an analytical fault detection system is the design of an algorithm which monitors signals of the process and decides whether a fault has occurred and if possible of what kind (Isermann, 2011). The traditional principal component analysis (PCA) assumes that the process is stationary and retains the most variability of the original data for large-scale systems. The adaptive versions of PCA allow the slow tracking of time-varying process parameters if more recent measurements are weighted more strongly than the old data (Tang, Yu, Chai, & Zhao, 2012). The adaptability of PCA is not enough for FD tasks, however, since the variations of actual multivariate observations could not allow the distinguishability between faults and normal process deviations. In particular, the new weighted recursive PCA, the topic of this discussion, is developed in order to address the rise of false alarms caused by small normal process changes as Portnoy, Melendez, Pinzon, and Sanjuan (2016) pointed out. The authors remarked in the last paragraph of Section 3, however, that the recursive update of PCA is executed when the operator identifies changes of the operation conditions but they are not faults. This means, the authors suggest considering the operator experience as a diagnostic tool without any systematic criterion to distinguish a fault from a deviation. This idea is in general wrong from a safety point of view. Even when the operators could have great experience, faults which have not occurred in the past could happen, generating high-risk decisions. We support the methods
⁎
Corresponding author. E-mail addresses: [email protected] (M. Quiñones-Grueiro), [email protected] (C. Verde).
http://dx.doi.org/10.1016/j.conengprac.2016.10.015 Received 22 April 2016; Accepted 23 October 2016 Available online xxxx 0967-0661/ © 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Quiñones-Grueiro, M., Control Engineering Practice (2016), http://dx.doi.org/10.1016/j.conengprac.2016.10.015
Control Engineering Practice xx (xxxx) xxxx–xxxx
M. Quiñones-Grueiro, C. Verde
References
Moreover, the absence of the use of the residual of the PCA model in the results with the WARP algorithm draws the attention, because the residual contains useful information for FD, as Gertler, Li, Huang, and McAvoy (1999) have pointed out. Since the monitoring algorithm proposed by Jeng (2010) involves a recursive PCA with a moving window, the discussion of the comparison with the WARP algorithm given in Section 4 could include details of how Jeng's algorithm is implemented, improving the discussion. Regarding the results obtained in the real case study (the natural gas transmission pipeline) there is a lack of information with respect to the number of measurements, the sampling time of the process and the number of principal components retained after the WARP algorithm is performed.
Gertler, J., Li, W., Huang, Y., & McAvoy, T. (1999). Isolation enhanced principal component analysis. AIChE Journal, 45(2), 323–334. Henley, E. J., (1984). Application of expert systems to fault diagnosis. In AIChE annual meeting. San Francisco, California. Isermann, R. (2011). Fault-diagnosis applications: model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems Berlin: Springer. Jeng, J. C. (2010). Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms. Journal of the Taiwan Institute of Chemical Engineers, 41(4), 475–481. Li, W., Yue, H., Valle-Cervantes, S., & Qin, S. (2000). Recursive {PCA} for adaptive process monitoring. Journal of Process Control, 10(5), 471–486. Mina, J., & Verde, C. (2007). Fault detection for large scale systems using DPCA. International Journal of Computers Communications and Control, 2(2), 185–194. Mina, J., Verde, C., Sánchez-Parra, M., & Ortega, F., (2008). Fault isolation with principal components structural models for a gas turbine. In American control conference. Seattle. Portnoy, I., Melendez, K., Pinzon, H., & Sanjuan, M. (2016). An improved weighted recursive PCA algorithm for adaptive fault detection. Control Engineering Practice, 50, 69–83. Tang, J., Yu, W., Chai, T., & Zhao, L. (2012). On-line principal component analysis with application to process modeling. Neurocomputing, 82, 167–178. Venkatasubramanian, V., Rengaswamyd, R., Yin, R., & Kavuri, S. (2003). A review of process fault detection and diagnosis: Part i quantitative model-based methods. Computers and Chemical Engineering, 27, 293–311. Wang, X., Wang, X., Wang, Z., & Qian, F. (2013). A novel method for detecting processes with multi-state modes. Control Engineering Practice, 21(12), 1788–1794.
2. Notation and display details We have identified some details which can confuse the reader, and they are given in the following list:
• • •
The indicators shown in Table 3 are not well displayed. The variable w can be misunderstood by the reader because it is used to define a random noise in the Eq. (64) and the weights of the WARP algorithm. The parameter Σ * is not defined in the paper.
Acknowledgement Project supported by the Mexican Government Scholarship Program for International Students, DGAPA-UNAM IT100716, IIUNAM and Universidad Tecnológica de La Habana José Antonio Echeverría (CUJAE).
2
Contents lists available at ScienceDirect
Control Engineering Practice journal homepage: www.elsevier.com/locate/conengprac
Comments on the applicability of “An improved weighted recursive PCA algorithm for adaptive fault detection” ⁎
Marcos Quiñones-Grueiroa, , Cristina Verdeb a b
Department of Automation and Computing, Cujae, Havana, Cuba Instituto de Ingeniera, UNAM, Mexico City, México
A R T I C L E I N F O
A BS T RAC T
Keywords: Data-driven fault detection False alarm rate Weighted recursive PCA Fault realization test Computational complexity
This paper discusses some limitations of the weighted recursive PCA algorithm (WARP) proposed by Portnoy, Melendez, Pinzon, and Sanjuan (2016) which is used for fault detection (FD) by arguing that it can reduce false alarms. The motivation of these comments is the lack of a clear criterion in the WARP algorithm to distinguish between process deviations and faults' scenarios, and as a consequence, the applicability of this algorithm is questionable from the FD point of view. Moreover, we address the absence of a formal justification why the computational complexity achieved by using the WARP algorithm is reduced in comparison with methods discussed in the paper.
1. Main discussion
based on expert systems for the FD task (Henley, 1984); however, this does not mean that every operator of an industrial system can decide on-line if a specific hazardous condition is present. Therefore from a safety point of view, the WARP algorithm is unsuitable for application in a real large-scale process. Do not forget that according to Venkatasubramanian, Rengaswamyd, Yin, and Kavuri (2003) 70% of the industrial accidents are provoked by human errors. As a consequence of the above facts, the FDI community suggests that additional process considerations must be included in the adaptive PCA schemes to achieve success from a fault detection point of view. As examples, the procedure presented in Wang, Wang, Wang, and Qian (2013) which considers multi-modes, or the algorithm published in Mina and Verde (2007) which assumes a static relation between some process variables can be applied. Moreover, structural analysis (SA) can be complemented with dynamic principal component analysis (DPCA) to tackle isolability issues of large-scale systems, as it is used by Mina, Verde, Sánchez-Parra, and Ortega (2008) for the specific case of a gas turbine. An important property of the WARP algorithm is the reduction of the computational complexity. One has to clarify, however, how it can be reduced in comparison with the method proposed by Li, Yue, ValleCervantes, and Qin (2000) if the set of equations used for the recursive update of the statistical parameters are similar in both algorithms. The use of the forgetting factor as w = 1 − μ or its complement μ cannot change the results and the computational magnitude order. A formal justification of why the WARP algorithm achieves a reduction in the computational complexity is missing in Section 4 of the paper.
The basic idea behind an analytical fault detection system is the design of an algorithm which monitors signals of the process and decides whether a fault has occurred and if possible of what kind (Isermann, 2011). The traditional principal component analysis (PCA) assumes that the process is stationary and retains the most variability of the original data for large-scale systems. The adaptive versions of PCA allow the slow tracking of time-varying process parameters if more recent measurements are weighted more strongly than the old data (Tang, Yu, Chai, & Zhao, 2012). The adaptability of PCA is not enough for FD tasks, however, since the variations of actual multivariate observations could not allow the distinguishability between faults and normal process deviations. In particular, the new weighted recursive PCA, the topic of this discussion, is developed in order to address the rise of false alarms caused by small normal process changes as Portnoy, Melendez, Pinzon, and Sanjuan (2016) pointed out. The authors remarked in the last paragraph of Section 3, however, that the recursive update of PCA is executed when the operator identifies changes of the operation conditions but they are not faults. This means, the authors suggest considering the operator experience as a diagnostic tool without any systematic criterion to distinguish a fault from a deviation. This idea is in general wrong from a safety point of view. Even when the operators could have great experience, faults which have not occurred in the past could happen, generating high-risk decisions. We support the methods
⁎
Corresponding author. E-mail addresses: [email protected] (M. Quiñones-Grueiro), [email protected] (C. Verde).
http://dx.doi.org/10.1016/j.conengprac.2016.10.015 Received 22 April 2016; Accepted 23 October 2016 Available online xxxx 0967-0661/ © 2016 Elsevier Ltd. All rights reserved.
Please cite this article as: Quiñones-Grueiro, M., Control Engineering Practice (2016), http://dx.doi.org/10.1016/j.conengprac.2016.10.015
Control Engineering Practice xx (xxxx) xxxx–xxxx
M. Quiñones-Grueiro, C. Verde
References
Moreover, the absence of the use of the residual of the PCA model in the results with the WARP algorithm draws the attention, because the residual contains useful information for FD, as Gertler, Li, Huang, and McAvoy (1999) have pointed out. Since the monitoring algorithm proposed by Jeng (2010) involves a recursive PCA with a moving window, the discussion of the comparison with the WARP algorithm given in Section 4 could include details of how Jeng's algorithm is implemented, improving the discussion. Regarding the results obtained in the real case study (the natural gas transmission pipeline) there is a lack of information with respect to the number of measurements, the sampling time of the process and the number of principal components retained after the WARP algorithm is performed.
Gertler, J., Li, W., Huang, Y., & McAvoy, T. (1999). Isolation enhanced principal component analysis. AIChE Journal, 45(2), 323–334. Henley, E. J., (1984). Application of expert systems to fault diagnosis. In AIChE annual meeting. San Francisco, California. Isermann, R. (2011). Fault-diagnosis applications: model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems Berlin: Springer. Jeng, J. C. (2010). Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms. Journal of the Taiwan Institute of Chemical Engineers, 41(4), 475–481. Li, W., Yue, H., Valle-Cervantes, S., & Qin, S. (2000). Recursive {PCA} for adaptive process monitoring. Journal of Process Control, 10(5), 471–486. Mina, J., & Verde, C. (2007). Fault detection for large scale systems using DPCA. International Journal of Computers Communications and Control, 2(2), 185–194. Mina, J., Verde, C., Sánchez-Parra, M., & Ortega, F., (2008). Fault isolation with principal components structural models for a gas turbine. In American control conference. Seattle. Portnoy, I., Melendez, K., Pinzon, H., & Sanjuan, M. (2016). An improved weighted recursive PCA algorithm for adaptive fault detection. Control Engineering Practice, 50, 69–83. Tang, J., Yu, W., Chai, T., & Zhao, L. (2012). On-line principal component analysis with application to process modeling. Neurocomputing, 82, 167–178. Venkatasubramanian, V., Rengaswamyd, R., Yin, R., & Kavuri, S. (2003). A review of process fault detection and diagnosis: Part i quantitative model-based methods. Computers and Chemical Engineering, 27, 293–311. Wang, X., Wang, X., Wang, Z., & Qian, F. (2013). A novel method for detecting processes with multi-state modes. Control Engineering Practice, 21(12), 1788–1794.
2. Notation and display details We have identified some details which can confuse the reader, and they are given in the following list:
• • •
The indicators shown in Table 3 are not well displayed. The variable w can be misunderstood by the reader because it is used to define a random noise in the Eq. (64) and the weights of the WARP algorithm. The parameter Σ * is not defined in the paper.
Acknowledgement Project supported by the Mexican Government Scholarship Program for International Students, DGAPA-UNAM IT100716, IIUNAM and Universidad Tecnológica de La Habana José Antonio Echeverría (CUJAE).
2