Industrial process fault diagnosis method based on switching linear dynamic system model

Abstract

The invention discloses an industrial process fault diagnosis method based on a switching linear dynamic system model. The method comprises the steps of firstly carrying out independent repeated sampling on normal operation data and various known fault data of the industrial process, and then establishing the switching linear dynamic system model through a learning algorithm of the switching linear dynamic system model; and then acquiring a diagnosis result of the current monitoring data by using a Gaussian sum filtering method, that is, judging whether the current data is located at a normal operating condition, and if not, judging which fault the current data is located. Compared with other method at present, the industrial process fault diagnosis method not only improves a fault diagnosis effect of the industrial process, enhances mastering of a process operator for the process state, enables industrial production to be safer, and enables the product quality to be more stable. In addition, the industrial process fault diagnosis method improves the dependency of a fault diagnosis method for process knowledge to a great extent, thereby being more conducive to automation implementation of the industrial process.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to an industrial process fault diagnosis method based on a switching linear dynamic system model. Background technique [0002] In recent years, the problem of fault diagnosis in industrial production process has been paid more and more attention by 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 diagnostic effect of a certain method is greatly limited. 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 fault diagnosis method and timely and correctly diagnosing faults have become one of the research hot...

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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