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Paper details
Number 1 - March 2021
Volume 31 - 2021
A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes
Lei Huang, Hao Ren, Yi Chai, Jianfeng Qu
Abstract
This paper proposes a fault detection method by extracting nonlinear features for nonstationary and stationary hybrid
industrial processes. The method is mainly built on the basis of a sparse auto-encoder and a sparse restricted Boltzmann
machine (SAE-SRBM), so as to take advantages of their adaptive extraction and fusion on strong nonlinear symptoms. In
the present work, SAEs are employed to reconstruct inputs and accomplish feature extraction by unsupervised mode, and
their outputs present a knotty problem of an unknown probability distribution. In order to solve it, SRBMs are naturally used
to fuse these unknown probability distribution features by transforming them into energy characteristics. The contribution
of this method is the capability of further mining and learning of nonlinear features without considering the nonstationary
problem. Also, this paper introduces a method of constructing labeled and unlabeled training samples while maintaining
time series features. Unlabeled samples can be adopted to train the part for feature extraction and fusion, while labeled
samples can be used to train the classification part. Finally, a simulation on the Tennessee Eastman process is carried
out to demonstrate the effectiveness and excellent performance on fault detection for nonstationary and stationary hybrid
industrial processes.
Keywords
fault detection, sparse auto-encoder, sparse restricted Boltzmann machine, hybrid industrial processes