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A Novel Fault Detection Model Based on Vector Quantization Sparse Autoencoder for Nonlinear Complex Systems
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DOI：10.1109/TII.2022.3174715.png)
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摘要
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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.
Keyword:
Fault detection
Feature extraction
Complex systems
Kernel
Data models
Process monitoring
Vector quantization
fault detection
local Mahalanobis distance
vector quantization sparse autoencoder
期刊
IF:
9.9
论文数:
8.6K
被引数:
6.0W
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