Fault Detection Based on Sliding Window and Multiblock Convolutional Autoencoders

Authors

Jianpeng Mou, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaFollow
Weili Xiong, School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China; Key Laboratory of Advanced Process Control for Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China

Abstract

Abstract: In order to further improve the fault detection performance and fully mine the timing and hidden feature information, a fault detection method based on convolutional auto encoder is proposed. On the basis of modeling the original information set, the modeling of cumulative information and rate of change information is added to enhance the mining of implicit information; The three reconstructed information sets are sampled by sliding windows, and time series feature extraction and modeling are performed based on convolutional auto encoders. Bayesian fusion of the decision results of the convolutional auto encoder is performed to obtain the statistics, and the control limit is determined by the method of kernel density estimation for fault detection. The method is numerically simulated and applied in TE process, and the simulation results confirm the effectiveness and detection performance.

Recommended Citation

Mou, Jianpeng and Xiong, Weili (2024) "Fault Detection Based on Sliding Window and Multiblock Convolutional Autoencoders," Journal of System Simulation: Vol. 36: Iss. 2, Article 12.
DOI: 10.16182/j.issn1004731x.joss.22-1059
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss2/12

First Page

423

Last Page

435

CLC

TP277

Recommended Citation

Mou Jianpeng, Xiong Weili. Fault Detection Based on Sliding Window and Multiblock Convolutional Autoencoders[J]. Journal of System Simulation, 2024, 36(2): 423-435.

DOI

10.16182/j.issn1004731x.joss.22-1059