A fault detection method based on stacking the SAE-SRBM for nonstationary and stationary hybrid processes - Huang, Lei; Ren, Hao; Chai, Yi; Qu, Jianfeng (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: fault detection; sparse auto-encoder; sparse restricted Boltzmann machine; hybrid industrial processes