Non-linear industrial process fault detection method based on Bayes kernel slow feature analysis

A fault detection and industrial process technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve problems such as inability to accurately extract non-linear characteristic information of data
CN106647718AActive Publication Date: 2017-05-10CHINA UNIV OF PETROLEUM (EAST CHINA)
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHINA UNIV OF PETROLEUM (EAST CHINA)
Publication Date
2017-05-10

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Abstract

The invention relates to a non-linear industrial process fault detection method based on Bayes kernel slow feature analysis. After normalization processing of training data and test data, kernel functions of different types are adopted based on a conventional kernel feature analysis method, and the various kernel functions are configured with different kernel functions, and therefore a series of basic KSFA models are established. Non-linear slow features are more fully extracted from the normalized training data and the normalized test data by using the basic KSFA models, and the basic KSFA models are respectively used to monitor the process. The non-linear industrial process fault detection method is provided with Bayesian inference, and the test data monitoring results of the series of basic KSFA models are weighted in a combined manner by adopting a probability way, and finally the integrated monitoring result of a plurality of models is acquired, and therefore a fault detection result is improved, and a fault detection rate is improved.
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Description

technical field

[0001] The invention belongs to the technical field of industrial process fault detection, and relates to a nonlinear industrial process fault detection method, in particular to a nonlinear industrial process fault detection method based on Bayesian kernel slow feature analysis. Background technique

[0002] As modern industrial systems tend to be highly integrated and large-scale, fault diagnosis of industrial processes has become a key technology to ensure the safe and stable operation of modern industrial systems. With the development of modern computer control technology, a wealth of process operation data is collected and stored in industrial processes. Therefore, data-driven fault detection methods and diagnostic techniques have gradually become a research hotspot in the field of industrial process monitoring. Researchers have proposed a series of data-driven fault detection and diagnosis methods, such as: Principal Component Analysis (PCA), Independen...

Claims

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