Flow industrial process fault detection method based on data missing

A process industry and fault detection technology, applied in the field of monitoring and safety production, and process control in process industry, can solve the problems of slow PCA model training and low accuracy, and achieve the effect of small calculation amount, fast operation speed and less computing resources

Active Publication Date: 2021-12-03
SHANGHAI INST OF TECH
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Problems solved by technology

However, the PCA model adopted by this method is slow to train and not very accurate

Method used

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  • Flow industrial process fault detection method based on data missing
  • Flow industrial process fault detection method based on data missing
  • Flow industrial process fault detection method based on data missing

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Embodiment Construction

[0087] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0088] Such as figure 1 As shown, the present invention provides a process fault detection method based on missing data. The working principle of the method is as follows: firstly, the normal data of the process industrial process is collected during normal operation, and the training data set containing the missing value is obtained through processing. The kernel extreme learning machine (KELM) based on model update fills in missing values ​​for each samp...

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Abstract

The invention relates to a flow industrial process fault detection method based on data missing. The method comprises the following steps: S1, performing data sampling and processing on a flow industrial process; S2, filling missing data in the sampling data by using a kernel extreme learning machine KELM; S3, carrying out low-dimensional feature extraction on the data by adopting a landmark equidistant mapping method L-ISOMAP; and S4, respectively calculating a statistical magnitude and a control magnitude in the feature space and the residual space, and carrying out fault detection. Compared with the prior art, the method has the advantages of being high in accuracy, saving time and computing resources and the like.

Description

technical field [0001] The invention relates to the field of flow industry process control, monitoring and safe production, in particular to a method for detecting process faults in the flow industry based on missing data. Background technique [0002] With the introduction of the concept of Industry 4.0 and the increasing maturity of technologies such as the Industrial Internet and the Internet of Things, the transformation of intelligent manufacturing in the industrial production process has become an inevitable trend in the development of traditional industries, and the result is that the industrial process has become more and more integrated. and large-scale. For example, the production process of oil refining, pharmaceutical and other process industries is becoming more and more complex, and it is becoming more and more difficult to establish an accurate mechanism model for the process through traditional methods. With the support of distributed control systems, data a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06F18/2135G06F18/10
Inventor 顾昊昱张成功钱平王丽
Owner SHANGHAI INST OF TECH
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