Detection method for micro faults in chemical process

A chemical process and fault detection technology, applied in electrical testing/monitoring, testing/monitoring control systems, instruments, etc., can solve the problem of low detection rate of small faults

Active Publication Date: 2019-09-17
CHINA UNIV OF PETROLEUM (EAST CHINA)
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AI Technical Summary

Problems solved by technology

[0004] The present invention aims at the problem of ignoring the probability information contained in the process data in the traditiona...

Method used

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  • Detection method for micro faults in chemical process
  • Detection method for micro faults in chemical process
  • Detection method for micro faults in chemical process

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Embodiment

[0125] The continuous stirred reactor (hereinafter referred to as: CSTR) system is a typical nonlinear chemical process control system, which is widely used in the field of fault detection and diagnosis. see figure 2 , The CSTR system includes a temperature and liquid level control loop, and substance A undergoes a first-order irreversible exothermic reaction in the reactor to form substance B. During the simulation, measurement noise is added to simulate normal and six fault conditions. The process data information is collected from 10 variables of the CSTR system, including 4 state variables and 6 input variables, see Table 1 for details. The 6 faults used in this embodiment are shown in Table 2, and the monitoring performance of each method is verified through the faults in Table 2. Both normal and fault conditions contain 1000 samples. First, 1000 sets of normal data are simulated as a training set for historical modeling. In order to generate fault data, a fault is int...

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Abstract

The invention relates to a detection method for micro faults in a chemical process. The method comprises the following steps: normalization processing is carried out on training data and then an LGPCA (local-global principal component analysis) model is established, a local-global feature is extracted from the training data and serves as a score vector, the mean value and the variance of the score vector of the training data are calculated through a sliding window, a training KLD (Kullback Leibler Divergence) component is obtained on the basis, further, main component space statistics T2 and residual space statistics SPE are calculated based on the training KLD component, and a corresponding control limit is determined; test data is collected, a corresponding main component vector and a residual vector are extracted by utilizing the LGPCA model, the mean value and the variance of the test data score vector are calculated by utilizing the sliding window, an online KLD component is further obtained, the main component space statistics T2 and the residual space statistics SPE are calculated on the basis of the online KLD component, and the control limit is used for monitoring. According to the method in the invention, the KLD is introduced into the traditional LGPCA method, the probability information contained in the chemical process data can be fully utilized, and the micro fault detection rate is improved.

Description

technical field [0001] The invention belongs to the technical field of chemical process fault detection, and in particular relates to a small fault detection method for chemical process. Background technique [0002] Due to the increasingly large and complex modern chemical process, once the process is abnormal and not controlled in time, it will cause huge loss of life and property. If small faults can be detected within the controllable range of the industrial operation process in time and isolated and alarmed, the occurrence of abnormal events will be effectively avoided and the loss of productivity will be reduced. As an important method and effective measure to improve system reliability and reduce accident risk, fault detection and diagnosis technology is becoming more and more important. With the widespread application of Distributed Control System (abbreviation: DCS), a large amount of process data is saved, and the fault diagnosis method based on data drive has bee...

Claims

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Application Information

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IPC IPC(8): G05B23/02
CPCG05B23/024
Inventor 邓晓刚蔡配配曹玉苹
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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