Industrial production process fault diagnosis method based on data driving

Abstract

The invention discloses an industrial production process fault diagnosis method based on data driving, and the method comprises the steps: calculating an average deviation and a variance, carrying outthe feature extraction of multi-dimensional data in an industrial production process, obtaining feature data, and building an original input sample set; Performing fault diagnosis on the industrial production process to be diagnosed by using the original input sample set and using the trained random forest model to obtain a diagnosis result; And analyzing and solving the cause of the fault in theindustrial production process to be diagnosed according to whether the diagnosis result has the fault and the fault type. According to the method, the particle swarm algorithm is adopted, two key parameters of the random forest model are optimized at the same time, a feasible and efficient method is provided for optimization of the random forest parameters, and the accuracy of fault diagnosis inthe industrial production process through the random forest algorithm is improved.

Description

technical field [0001] The invention belongs to the field of industrial production process diagnosis, and more particularly relates to a data-driven industrial production process fault diagnosis method. Background technique [0002] The industrial production process system is becoming more and more complex, and the various process procedures are interrelated and affect each other. Once any of the processes fails, it will cause the system to fail, affect normal production, cause major economic losses to the enterprise, and even cause personnel safety accidents in severe cases. , Bring losses to the country and the people. Therefore, from the perspective of safe production and enterprise economic benefits, it is very necessary to carry out fault diagnosis through the analysis of industrial production process data. [0003] Existing fault diagnosis methods can be divided into methods based on mechanism models, methods based on knowledge, methods based on signal processing and ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above deficiencies or improvement needs of the prior art, the present invention provides a data-driven fault diagnosis method for industrial production processes, thereby solving the large deviation of the diagnosis results of the existing fault diagnosis method and the low efficiency of parameter optimization of the diagnosis algorithm technical issues

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