Industrial process fault detection method based on multiple classifiers and D-S evidence fusion

Abstract

The invention discloses an industrial process fault detection method based on multiple classifiers and the D-S evidence fusion. The industrial process fault detection method includes the steps of firstly, independently and repeatedly sampling normal data in the industrial process, then building corresponding classifier models for new training model data with multiple classifier methods, integrating and combining multiple classifier decisions through the D-S evidence theory, and obtaining final monitoring results. Compared with other methods at present, the industrial process fault detection method has the advantages that the monitoring effect in the industrial process can be greatly improved, the delay detection time can be shortened, the monitoring performance is improved to a great degree, the comprehensive ability and the operation confidence of a process operator to the process are improved, and the industrial process can be easily and automatically implemented.

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 multi-classifiers and D-S evidence 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 urgent...

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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