## Abstract and Applied Analysis

### A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure

#### Abstract

Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric learning, MTS pieces building, and MTS classification is presented. After that, we conduct experiments on industrial benchmark of Tennessee Eastman (TE) process. The experimental results demonstrate the improved performance of the proposed algorithm when compared with other classical PM-FD classical methods.

#### Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 625814, 8 pages.

Dates
First available in Project Euclid: 6 October 2014

https://projecteuclid.org/euclid.aaa/1412606637

Digital Object Identifier
doi:10.1155/2014/625814

Zentralblatt MATH identifier
07022760

#### Citation

Mei, Jiangyuan; Hou, Jian; Karimi, Hamid Reza; Huang, Jiarao. A Novel Data-Driven Fault Diagnosis Algorithm Using Multivariate Dynamic Time Warping Measure. Abstr. Appl. Anal. 2014 (2014), Article ID 625814, 8 pages. doi:10.1155/2014/625814. https://projecteuclid.org/euclid.aaa/1412606637

#### References

• S. Yin, X. Yang, and H. R. Karimi, “Data-driven adaptive observer for fault diagnosis,” Mathematical Problems in Engineering, vol. 2012, Article ID 832836, 21 pages, 2012.
• S. Yin, S. X. Ding, A. H. A. Sari, and H. Hao, “Data-driven monitoring for stochastic systems and its application on batch process,” International Journal of Systems Science, vol. 44, no. 7, pp. 1366–1376, 2013.
• V. Venkatasubramanian, R. Rengaswamy, K. Yin, and S. N. Kavuri, “A review of process fault detection and diagnosis part I: quantitative model-based methods,” Computers and Chemical Engineering, vol. 27, no. 3, pp. 293–311, 2003.
• W. Chen and M. Saif, “Observer-based fault diagnosis of satellite systems subject to time-varying thruster faults,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 129, no. 3, pp. 352–356, 2007.
• P. Zhang, H. Ye, S. X. Ding, G. Z. Wang, and D. H. Zhou, “On the relationship between parity space and H$_{2}$ approaches to fault detection,” Systems and Control Letters, vol. 55, no. 2, pp. 94–100, 2006.
• J. Suonan and J. Qi, “An accurate fault location algorithm for transmission line based on R-L model parameter identification,” Electric Power Systems Research, vol. 76, no. 1–3, pp. 17–24, 2005.
• M. A. Schwabacher, “A survey of data-driven prognostics,” in Proceedings of the Advancing Contemporary Aerospace Technologies and Their Integration (InfoTech '05), pp. 887–891, September 2005.
• S. Yin, G. Wang, and H. R. Karimi, “Data-driven design of robust fault detection system for wind turbines,” Mechatronics. In press.
• S. Yin, H. Luo, and S. Ding, “Real-time implementation of fault-tolerant control systems with performance optimization,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2402–2411, 2012.
• L. H. Chiang, E. L. Russell, and R. D. Braatz, “Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis,” Chemometrics and Intelligent Laboratory Systems, vol. 50, no. 2, pp. 243–252, 2000.
• M. Misra, H. H. Yue, S. J. Qin, and C. Ling, “Multivariate process monitoring and fault diagnosis by multi-scale PCA,” Computers and Chemical Engineering, vol. 26, no. 9, pp. 1281–1293, 2002.
• S. Ding, P. Zhang, E. Ding et al., “On the application of PCA technique to fault diagnosis,” Tsinghua Science and Technology, vol. 15, no. 2, pp. 138–144, 2010.
• W. Ku, R. H. Storer, and C. Georgakis, “Disturbance detection and isolation by dynamic principal component analysis,” Chemometrics and Intelligent Laboratory Systems, vol. 30, no. 1, pp. 179–196, 1995.
• Y. Zhang, H. Zhou, S. J. Qin, and T. Chai, “Decentralized fault diagnosis of large-scale processes using multiblock kernel partial least squares,” IEEE Transactions on Industrial Informatics, vol. 6, no. 1, pp. 3–10, 2010.
• M. R. Maurya, R. Rengaswamy, and V. Venkatasubramanian, “Fault diagnosis using dynamic trend analysis: a review and recent developments,” Engineering Applications of Artificial Intelligence, vol. 20, no. 2, pp. 133–146, 2007.
• S. Yin, S. X. Ding, P. Zhang, A. Hagahni, and A. Naik, “Study on modifications of pls approach for process monitoring,” Threshold, vol. 2, pp. 12389–12394, 2011.
• M. Kano, S. Tanaka, S. Hasebe, I. Hashimoto, and H. Ohno, “Monitoring independent components for fault detection,” AIChE Journal, vol. 49, no. 4, pp. 969–976, 2003.
• J.-M. Lee, S. J. Qin, and I.-B. Lee, “Fault detection and diagnosis based on modified independent component analysis,” AIChE Journal, vol. 52, no. 10, pp. 3501–3514, 2006.
• Q. P. He, S. J. Qin, and J. Wang, “A new fault diagnosis method using fault directions in Fisher discriminant analysis,” AIChE Journal, vol. 51, no. 2, pp. 555–571, 2005.
• S. X. Ding, P. Zhang, A. Naik, E. L. Ding, and B. Huang, “Subspace method aided data-driven design of fault detection and isolation systems,” Journal of Process Control, vol. 19, no. 9, pp. 1496–1510, 2009.
• T. G. Holt, M. Reinders, and E. Hendriks, “Multi-dimensional dynamic time warping for gesture recognition,” in Proceedings of the 13th Annual Conference of the Advanced School for Computing and Imaging, vol. 119, 2007.
• S. Salvador and P. Chan, “Toward accurate dynamic time warping in linear time and space,” Intelligent Data Analysis, vol. 11, no. 5, pp. 561–580, 2007.
• G. Al-Naymat, S. Chawla, and J. Taheri, “Sparsedtw: a novel approach to speed up dynamic time warping,” in Proceedings of the 8th Australasian Data Mining Conference, vol. 101, Computer Society, Australian, 2009.
• L. H. Chiang, R. D. Braatz, and E. L. Russell, Fault Detection and Diagnosis in Industrial Systems, Springer, 2001.
• J. J. Downs and E. F. Vogel, “A plant-wide industrial process control problem,” Computers and Chemical Engineering, vol. 17, no. 3, pp. 245–255, 1993.
• S. Yin, S. X. Ding, A. Haghani, H. Hao, and P. Zhang, “A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process,” Journal of Process Control, vol. 22, no. 9, pp. 1567–1581, 2012.
• J. Mei, M. Liu, H. R. Karimi, and H. Gao, Logdet Divergence Based Metric Learning Using Triplet Labels, ICML Workshop on Divergences. \endinput