Fault detection method based on analytic hierarchy process and weighted vote decision fusion

Abstract

The invention discloses a fault detection method based on an analytic hierarchy process and weighted vote decision fusion. According to the method, firstly, multiple classifiers are selected as fusion sub-classifiers, multiple classifier models are established through a training data set, corresponding evaluation indexes are obtained according to the classification result of each classifier, and then multiple classifiers are scored and sequenced with the analytic hierarchy process, so that each classifier is endowed with the corresponding weight. Finally, multiple classifier decision results are integrated with a weighted vote fusion method, and the final fault detection result is obtained. Compared with other methods at present, the fault detection method has the advantages that the monitoring effect of the industrial process is improved, mastering and operation confidence of a process operator for the process are enhanced, the limitation of single fault detection methods is overcome to a great extent, and automatic implementation of the industrial process is better facilitated.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to an industrial process fault detection method based on the fusion of analytic hierarchy process and weighted voting decision. 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 ...

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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