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Fault detection method for chemical process based on multi-sampling probability kernel principal component model

A chemical process and nuclear principal component technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as the inability to judge product quality in the production process, improve online detection efficiency and performance, improve results, and stabilize product quality monitoring Effect

Active Publication Date: 2021-07-02
ZHEJIANG JINGXING PAPER
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Problems solved by technology

However, this type of method cannot judge whether the fluctuation of the production process affects the quality of the final product

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  • Fault detection method for chemical process based on multi-sampling probability kernel principal component model
  • Fault detection method for chemical process based on multi-sampling probability kernel principal component model
  • Fault detection method for chemical process based on multi-sampling probability kernel principal component model

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Embodiment Construction

[0109] Taking the synthetic ammonia production process as an example, the present invention is further described:

[0110] A fault detection method for synthetic ammonia production process based on the multi-sampling probability kernel principal component analysis model. The method aims at the fault detection problem of the synthetic ammonia process. Principal component analysis model. The model structure is estimated by the expectation maximization algorithm. On this basis, two detection statistics T 2 and SPE and their corresponding statistical limits and SPE lim . The on-line synthetic ammonia production process is detected to obtain test samples, and then the latent variables and prediction errors of the test samples can be estimated by using the existing model structure, and the corresponding statistics are calculated, and the final fault detection results are obtained.

[0111] see figure 1 , the present invention a kind of synthetic ammonia production process fau...

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Abstract

The invention discloses a chemical process fault detection method based on a multi-sampling probability kernel principal component model. First, a multi-sampling probability kernel principal component analysis model is constructed by collecting training sample sets, and the T of the training samples is obtained. 2 and SPE statistic detection control limits; then collect the process parameters in the actual operation process of the chemical process to be tested online to obtain the test samples, and calculate the T of the test samples 2 and SPE statistics, and finally compared with the obtained detection control limit, to obtain the online detection results of the chemical process. The present invention uses the kernel learning method based on the multi-sampling probability kernel principal component analysis model to establish an effective nonlinear fault detection model, overcomes the problems caused by different sampling rates in the chemical production process, and improves the online detection efficiency of the process And performance, so that the chemical production process is more reliable, and the product quality monitoring is more stable.

Description

technical field [0001] The invention relates to a fault detection method, in particular to a chemical process fault detection method based on a multi-sampling probability kernel principal component model. Background technique [0002] In the modern process industry, with the application of the distributed control system (DCS) and the advancement of computer technology, a large amount of on-line and off-line measurement data are collected and stored on the industrial site, and the process monitoring (MSPM) technology based on multivariate statistical analysis has been rapidly developed. It has the advantages of data-based, dimensionality reduction, easy visualization, and easy practical application. It has been widely used in many industrial fields such as chemical industry, pharmaceuticals, and semiconductor manufacturing. Among them, Principal Component Analysis (PCA) and Partial Least Square Estimation (PLS) and their extended methods are representative models of MSPM tech...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/214G06F18/2453
Inventor 周乐谢佳敏介婧侯北平
Owner ZHEJIANG JINGXING PAPER
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