Independent component analysis - Based sparse autoencoder in the application of fault diagnosis

Abstract

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In the majority of the multivariable processes, analysis of process monitoring and fault diagnosis is usually based on the fundamental assumption that the monitored variables follow a Gaussian distribution. However, it is well known that many of the variables are mutually dependent in process systems. This paper proposes a new monitoring method based on independent component analysis (ICA) - sparse autoencoder. The independent information component can be extracted by ICA through higher-order statistics. Moreover, the inherent nonlinear characteristics in the residual model of ICA can be handled by a deep architecture constructed with sparse autoencoder. To overcome the problem of local minima in the optimization of sparse autoencoder, a restricted Boltzmann machine (RBM) is used to pre-train the net, and the parameters in the sparse autoencoder is updated by Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Monitoring statistic is developed and its confidence limit is computed by kernel density estimation. A case study of the Tennessee Eastman (TE) benchmark process indicates that the proposed fault detection method is more efficient.