Abstract
The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multilayer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multilayer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multilayer linear algorithms.
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This work was supported by the funding from the European Union’s Horizon 2020 research and innovation programme (No. 675215-PRONTO-H2020-MSCA-ITN-2015).
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Recommended by Associate Editor Jie Zhang
Ruo-Mu Tan received the B. Eng. degree in automation from Zhejiang University, China in 2013, and the M. Sc. degree in process control from the University of Alberta, Canada in 2015. She is currently a Ph. D. degree candidate at chemical engineering at Imperial College London, UK. Meanwhile, she is also a Marie Curie Early Stage researcher of Horizon 2020 Innovative Training Networks–European Industrial Doctorates Project PRONTO (i.e., Process network optimization for efficient and sustainable operation of Europe’s process industries taking machinery condition and process performance into account).
Her research interests include datadriven nonlinear process monitoring, multivariate statistical analysis and their application to process industries.
Yi Cao received the M. Sc. degree in control engineering from Zhejiang University, China in 1985 and the Ph. D. degree in engineering from the University of Exeter, UK in 1996. He is a professor in College of Chemical and Biological Engineering, Zhejiang University, China.
His research interests include plantwide control, nonlinear system identification, nonlinear model predictive control and process monitoring.
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Tan, RM., Cao, Y. Multi-layer Contribution Propagation Analysis for Fault Diagnosis. Int. J. Autom. Comput. 16, 40–51 (2019). https://doi.org/10.1007/s11633-018-1142-y
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DOI: https://doi.org/10.1007/s11633-018-1142-y