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

Active Publication Date: 2017-05-10
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

[0004] The present invention aims at the problem that the existing kernel-slow feature method is difficult to accurately select the kernel function and the corresponding kernel parameters in the nonlinear industrial process fault detection, resulting in the inability to accurately extract the nonlinear feature information contained in the data, and provides a Bayesian-based The non-linear industrial process fault detection method based on the Sri Lankan slow feature analysis, which can more accurately and effectively obtain the nonlinear feature information contained in the data, improve the fault detection rate, and then improve the fault detection results

Method used

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

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Embodiment 1

[0089] Embodiment 1: A nonlinear numerical system is taken as an example for illustration. simulates a system with three monitored variables x 1 ,x 2 ,x 3 The nonlinear numerical system of , its mathematical description is as follows:

[0090]

[0091] where e 1 ,e 2 ,e 3 ∈N(0,0.01) represents three independent noise variables, t∈[0,2] is a uniformly distributed random variable, and the output of the system [x 1 ,x 2 ,x 3] as a process monitoring variable. 300 samples were simulated under normal operating conditions as shown in equation (25) as training data for modeling. In order to generate fault data, at the 51st time in the simulation process, the variable x 1 Add ramp fault F1 and keep fault F1 until the end of the simulation at time 300.

[0092] The fault detection method for the above nonlinear numerical system includes the following steps:

[0093] (1) Collect the normal operating condition data of the nonlinear numerical system as the training data X ...

Embodiment 2

[0162] Embodiment two: Take the continuous stirred reactor (CSTR) system as an example, see Figure 4 , in the CSTR system, material A enters the reactor, a first-order irreversible chemical reaction occurs, material B is generated, heat is released, and the reactor is cooled by the jacketed coolant outside. In order to ensure the normal operation of the process, a cascade control system is adopted Control the liquid level and temperature of the reactor.

[0163] According to the process mechanism, the dynamic mechanism model of the CSTR system is established as follows:

[0164]

[0165]

[0166]

[0167]

[0168] In the formula, A is the cross-sectional area of ​​the reactor, c A is the concentration of material A in the reactor, c AF is the concentration of material A in the feed, C p is the specific heat of the reactants, C pC is the coolant specific heat, E is the activation energy, h is the reactor liquid level, k 0 is the response factor, Q F Feed flow...

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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.

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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 邓晓刚张汉元曹玉苹田学民
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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