A New Approach for Fault Diagnosis of Industrial Processes During Transitions

This paper presents a new approach for fault diagnosis of industrial processes during transitions. The proposed diagnosis strategy is based on the combination of the nearest-neighbor classification rule and the multivariate Dynamic Time Warping time series similarity measure. The proposal is compared with four different classification methods: Bayes Classifier, Multi-Layer Perceptron Neural Network, Support Vector Machines and Long Short-Term Memory Network which have high performance in the specialized scientific bibliography. The continuous stirred tank heater benchmark is used under scenarios of faults occurring at different moments of a transition and scarce fault data. The proposed approach achieves a classification performance approximately 20% superior compared to the best results of the four instance-based classifiers.

[1]  Rajagopalan Srinivasan,et al.  Online fault diagnosis and state identification during process transitions using dynamic locus analysis , 2006 .

[2]  Rajagopalan Srinivasan,et al.  Monitoring transitions in chemical plants using enhanced trend analysis , 2003, Comput. Chem. Eng..

[3]  Bernhard Schölkopf,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[4]  Nina F. Thornhill,et al.  A continuous stirred tank heater simulation model with applications , 2008 .

[5]  Geoff Dougherty,et al.  Pattern Recognition and Classification , 2013, Springer New York.

[6]  Chunjie Yang,et al.  Multimode Process Monitoring Approach Based on Moving Window Hidden Markov Model , 2018 .

[7]  Zhiqiang Ge,et al.  Distributed model projection based transition processes recognition and quality-related fault detection , 2016 .

[8]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[9]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[10]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[11]  Eamonn J. Keogh,et al.  Extracting Optimal Performance from Dynamic Time Warping , 2016, KDD.

[12]  Hongbo Shi,et al.  Key principal components with recursive local outlier factor for multimode chemical process monitoring , 2016 .

[13]  Eamonn J. Keogh,et al.  Experimental comparison of representation methods and distance measures for time series data , 2010, Data Mining and Knowledge Discovery.

[14]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .