Industrial process fault classification method based on analytic hierarchy process and fuzzy fusion

Abstract

The invention discloses an industrial process fault classification method based on analytic hierarchy process and fuzzy fusion. The method comprises the steps that a training data set is used to carry out offline modeling on a number of classifier methods to acquire a number of models; the classification performance of the classifiers is presented in the form of a fusion matrix; and the analytic hierarchy process is used to score and evaluate a number of classifier models, so that a corresponding weight is given to each classifier; the classifier models are called; a discriminant matrix is calculated according to the classification result of each classifier; and the discriminant matrix and the scoring result of the analytic hierarchy process are used to integrate the classification results of a number of classifier through a fuzzy fusion method to acquire a final fault classification result. Compared with other methods, the method provided by the invention has the advantages that the diagnostic effect of an industrial process is improved; the process operator's confidence in grasping and operating a process is enhanced; and the limitation of a single fault classification method is greatly improved, which is conductive to automated implementation of industrial processes.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to an industrial process fault classification method based on analytic hierarchy process and fuzzy fusion. Background technique [0002] In recent years, the monitoring of industrial production process has been paid more and more attention by the industry and academia. On the one hand, the actual industrial process is complex, has many operating variables, and has nonlinear, non-Gaussian, and dynamic stages. Under a single assumption, the monitoring effect of a certain method has great limitations. On the other hand, if the process is not well monitored and possible faults are diagnosed, operational accidents may occur, which may affect the quality of the product in the slightest, and cause loss of life and property in the severest case. Therefore, finding a better process monitoring method and making timely and correct forecasts has become one of the research hotspots and...

AI Technical Summary

Problems solved by technology

Traditional monitoring methods assume that the process operates under a single condition, which can no longer meet the monitoring requirements of actual industrial processes

Even if the different operating conditions of the process are modeled separately, satisfactory monitoring results cannot be achieved

Because when monitoring new process data, it is necessary to combine process knowledge to judge the working conditions of the data and select the corresponding monitoring model, which greatly enhances the dependence of monitoring methods on process knowledge, which is not conducive to the automation of industrial processes implement

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