Kernel Generalization of Multi-Rate Probabilistic Principal Component Analysis for Fault Detection in Nonlinear Process

In practical process industries, a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes, which indicates that the measurements coming from different sources are collected at different sampling rates. To build a complete process mo...

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Bibliographic Details
Published in:IEEE/CAA journal of automatica sinica 2021-08, Vol.8 (8), p.1465-1476
Main Authors: Zheng, Donglei, Zhou, Le, Song, Zhihuan
Format: Article
Language:English
Subjects:
Algorithms
Ammonia
Analytical models
Control equipment
Decarburization
Fault detection
Feature extraction
kernel method
Kernels
Measuring instruments
Monitoring
multi-rate process
Numerical models
Principal components analysis
Probabilistic logic
probability principal component analysis (PPCA)
Probability theory
Process control
Process controls
Process monitoring
Sampling
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Summary:In practical process industries, a variety of online and offline sensors and measuring instruments have been used for process control and monitoring purposes, which indicates that the measurements coming from different sources are collected at different sampling rates. To build a complete process monitoring strategy, all these multi-rate measurements should be considered for data-based modeling and monitoring. In this paper, a novel kernel multi-rate probabilistic principal component analysis (K-MPPCA) model is proposed to extract the nonlinear correlations among different sampling rates. In the proposed model, the model parameters are calibrated using the kernel trick and the expectation-maximum (EM) algorithm. Also, the corresponding fault detection methods based on the nonlinear features are developed. Finally, a simulated nonlinear case and an actual pre-decarburization unit in the ammonia synthesis process are tested to demonstrate the efficiency of the proposed method.
ISSN:2329-9266
2329-9274
DOI:10.1109/JAS.2021.1004090