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Chemical process fault detection method based on sparse filtering and logic regression

A logistic regression, chemical process technology, applied in electrical testing/monitoring and other directions, can solve problems such as poor generalization ability, complex modeling, and difficult industrial processes

Active Publication Date: 2017-12-05
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the generalization ability of many existing technologies is not good, and the modeling is complicated, so it is difficult to apply to industrial processes

Method used

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  • Chemical process fault detection method based on sparse filtering and logic regression
  • Chemical process fault detection method based on sparse filtering and logic regression
  • Chemical process fault detection method based on sparse filtering and logic regression

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Embodiment

[0073] This embodiment provides a chemical process fault detection method based on sparse filtering and logistic regression, the flow chart of the method is as follows figure 1 As shown, the method proposed in this embodiment is applied to the Tennessee-Eastman (TE) chemical process to further illustrate the method of this embodiment, and the TE process was published in 1993 by Downs and Vogel of Eastman Chemical Company in the United States The computer simulation of the actual chemical process of Eastman Company, which was later mainly developed to evaluate the performance of process control technology and process monitoring methods, the process flow chart of the process is as follows figure 2 shown. The TE process mainly includes five operating units, namely: reactor, condenser, vapor-liquid separator, cycle compressor, and stripper. In the simulated data, a total of 41 observed variables were monitored, including 22 continuous process variables and 19 component variables...

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Abstract

The invention discloses a chemical process fault detection method based on sparse filtering and logic regression. The method comprises steps of data acquisition and pre-processing, characteristic learning and characteristic classification, and chemical process fault detection is realized through the three steps. According to the method, firstly, the sparse filtering algorithm of depth learning is applied to chemical process characteristic learning, secondly, the learned characteristics are classified through utilizing logic regression, the characteristic learning stage utilizes monitoring-less learning, artificial data marking is not needed, characteristics of adaptive learning original data can be realized, normal data can be distinguished from each type of fault data, and application to the industry is more simple and intelligent.

Description

technical field [0001] The invention relates to the field of chemical process fault detection and diagnosis, in particular to a chemical process fault detection method based on sparse filtering and logistic regression. Background technique [0002] Process safety has always been one of the most important concerns in the modern chemical industry. Fault detection and diagnosis, as the most powerful tool for the management of chemical abnormal working conditions, provide a certain guarantee for process safety. With the rapid development of control systems, chemical processes are becoming more and more automated. Fault Detection and Diagnosis (FDD) has been proposed for more than 40 years, but FDD in actual production cannot be automated, which has a lot to do with the complexity of the process and the applicability of the method. [0003] Data-based chemical process fault detection methods can be applied to complex chemical process systems without the need to obtain a large a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B23/02
Inventor 旷天亮李秀喜詹志新
Owner SOUTH CHINA UNIV OF TECH
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