Parameter-optimized machine learning fault diagnosis system

Abstract

The invention discloses a parameter-optimized machine learning fault diagnosis system. The system is used for fault diagnosis of the ethylene cracking process and comprises a data preprocessing module, a principal component analysis module, a machine learning module and a swarm Intelligence algorithm module. Fault diagnosis is performed on important parameter indexes in the chemical process of ethylene cracking, the defects that existing chemical fault diagnosis technologies are low in instrument prediction accuracy and are easily affected by human factors are overcome, the swarm Intelligencealgorithm module is introduced to optimize parameters of a support vector machine, so that the parameter-optimized machine learning fault diagnosis system is obtained. The system has good prediction effect under the condition of small samples and easily finds the global optimal solution.

Description

technical field [0001] The invention relates to the fields of fault diagnosis, machine learning and swarm intelligence optimization algorithm, in particular to a chemical fault diagnosis system for ethylene cracking process combined with machine learning and swarm intelligence optimization algorithm. Background technique [0002] Chemical industry has penetrated into almost every aspect of people's life and work, and also involves various fields such as national industry, national defense, and agriculture. The chemical process needs to ensure high safety, because once a fault occurs, if the diagnosis is not timely and the fault cannot be eliminated, it will cause serious impacts, and even endanger people's personal and property safety, and the consequences will be disastrous. Therefore, the problem of fault diagnosis and monitoring of chemical process has been paid more and more attention by people. Experts and scholars at home and abroad have carried out in-depth discussion...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the disadvantages of low prediction accuracy and being easily affected by human factors in the existing fault diagnosis technology, the purpose of the present invention is to provide a fault diagnosis technology with high prediction accuracy, low false alarm rate, low false alarm rate, and strong anti-interference ability. Self-learning Fault Diagnosis System with Optimal Parameters

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