Industrial process fault detection and diagnosis framework based on enhanced supervised kernel entropy component analysis

Most existing industrial process fault detection and diagnosis (FDD) techniques operate on data collected at a single scale and focus only on known faults. However, actual process data are inherently multiscale and unknown faults are always inevitable during system running. Therefore, they may perfo...

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Bibliographic Details
Published in:Measurement : journal of the International Measurement Confederation 2022-06, Vol.196, p.111181, Article 111181
Main Authors: Xu, Peng, Liu, Jianchang, Shang, Liangliang, Zhang, Wenle
Format: Article
Language:English
Subjects:
Continuously stirred tank reactors
Data-dependent kernel
Entropy
Fault detection
Fault diagnosis
Faults
Kernel entropy component analysis (KECA)
Kernels
Monitoring
Multiscale principal component analysis (MSPCA)
Principal components analysis
Process monitoring
Unknown fault diagnosis
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Summary:Most existing industrial process fault detection and diagnosis (FDD) techniques operate on data collected at a single scale and focus only on known faults. However, actual process data are inherently multiscale and unknown faults are always inevitable during system running. Therefore, they may perform unsatisfactorily. To tackle this problem, this paper develops a decentralized industrial process FDD framework using multiple enhanced supervised kernel entropy component analysis (enhanced SKECA) models, where each model acts as a fault indicator for one specific fault. Faults can be easily diagnosed by monitoring the outputs of all models within the framework. In particular, when new faults are identified, the framework can update itself only by adding the corresponding enhanced SKECA models without a complete rebuilding process. The monitoring results for the continuous stirred tank reactor (CSTR) process show that the proposed framework is effective in diagnosing both known and unknown faults. •A decentralized industrial process FDD framework based on enhanced SKECA is proposed.•Enhanced SKECA boasts high classification accuracy.•Enhanced SKECA shows good robustness to the kernel size parameter.•Enhanced SKECA is sensitive to unknown data.•The proposed framework is effective in diagnosing both known and unknown faults.
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2022.111181