A Novel Fault Detection Model Based on Vector Quantization Sparse Autoencoder for Nonlinear Complex Systems

doi:10.1109/TII.2022.3174715

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A Novel Fault Detection Model Based on Vector Quantization Sparse Autoencoder for Nonlinear Complex Systems

2023-03-01

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DOI：10.1109/TII.2022.3174715

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Abstract

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To solve the problem of nonlinear factors in the fault detection process of complex systems, this article proposes a fault detection model based on vector quantization sparse autoencoder. First, a feature extraction model, which consists of a self-normalizing convolutional autoencoder module, a vector quantization module, a gradient module, and a loss module, is developed. The first module employs self-normalizing convolutional layers with good stability and generalization ability to extract the nonlinear structural features of complex systems. A nearest neighbor search strategy is implemented in the vector quantization module to further mine the nonlinear information. The gradient module adopts a straight-through estimation technique to improve the training efficiency. Sparse constraints are introduced into the loss module to obtain the essential features and enhance interpretability. Thereafter, a construction rule based on local Mahalanobis distance and K nearest neighbors is designed to calculate K Mahalanobis neighbor metrics that depend on the sparse features obtained by the feature extraction model. A comprehensive statistic for fault detection is constructed to accurately track the operating status of complex systems by combining the loss metric and the K Mahalanobis neighbor metric. Finally, the threshold of the fault detection statistics is determined by modeling the generalized extreme value distribution. Three case studies, a numerical simulation, the Tennessee Eastman benchmark process, and a typical circuit system, are adopted to demonstrate the effectiveness and merits of the proposed fault detection model.

Keywords:

Fault detection

Feature extraction

Complex systems

Kernel

Data models

Process monitoring

Vector quantization

fault detection

local Mahalanobis distance

vector quantization sparse autoencoder