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Chemical process failure detection method based on failure-dependent principal component space

A fault detection and chemical process technology, which is applied in the field of chemical process fault detection based on the fault-related principal component space, can solve the problems of redundancy, low fault detection efficiency and accuracy, and insensitivity to faults. The effect of industrial production safety and redundancy reduction

Inactive Publication Date: 2017-03-22
SHANGHAI DIANJI UNIV
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Problems solved by technology

The traditional PCA chemical process fault detection method has redundancy, is not sensitive to faults, and has low efficiency and accuracy of fault detection

Method used

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  • Chemical process failure detection method based on failure-dependent principal component space
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  • Chemical process failure detection method based on failure-dependent principal component space

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Embodiment Construction

[0049] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further elaborated below in conjunction with illustrations and specific embodiments.

[0050] Such as figure 1 As shown, a kind of chemical process fault detection method based on the fault-related principal component space proposed by the present invention comprises the following steps:

[0051] Step 1. Based on historical data under normal conditions, PCA is used to construct the principal component space and residual space.

[0052] Suppose the data matrix can be expressed as X∈R n×m, n is the number of historical normal data, m is the number of observed variables; normalize the mean variance of the historical normal data, that is, each data subtracts its respective mean value, and then divides it by its respective variance; through PCA, the data matrix X is decomposed into the sum of the outer products of m ...

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Abstract

The invention discloses a chemical process failure detection method based on a failure-dependent principal component space. The method comprises the following steps: constructing a principal component space and a residual space by PCA (Principal Component Analysis) according to history data in a normal state; optimizing the principal component space by a GA (Genetic Algorithm) according to history data in a failure state to obtain a failure-dependent principal component; constructing statistics in each failure-dependent principal component sub-space and the residual space; fusing the statistics of each failure-dependent principal component sub-space and the residual space into comprehensive statistics by a Bayes method; and calculating the comprehensive statistics according to online acquired data during online monitoring, and judging a running state. Through adoption of the chemical process failure detection method, a failure detection space is constructed by history normal data, and the failure detection space is optimized by the GA with history failure data, so that redundancy of failure detection is lowered, and the efficiency and accuracy of the failure detection are increased.

Description

technical field [0001] The invention relates to the field of intelligent information processing, in particular to a chemical process fault detection method based on fault-related principal component space. Background technique [0002] In recent years, extensive research has been carried out on the fault detection of chemical process at home and abroad. The main methods are as follows: [0003] (1) Method based on mathematical model. Establish a dynamic mathematical model of the system, use the detected or estimated physical parameters to reconstruct the state of the system, and predict, detect and diagnose process faults through the relationship between parameter changes and faults. [0004] (2) Methods based on prior knowledge. Modern industrial systems are large in scale and complex in structure, and it is difficult to establish a dynamic mathematical model of the system. The method based on prior knowledge does not require a dynamic mathematical model, so it has become...

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Application Information

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IPC IPC(8): G06F17/50G06F17/30G06Q10/04
CPCG06Q10/04G06F16/2462G06F30/367G06F2111/08G06F2117/02
Inventor 王洋姜庆超
Owner SHANGHAI DIANJI UNIV
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