Non-linear process industry fault prediction method based on novel FDE-ELM and EFSM

Abstract

The invention provides a non-linear process industry fault prediction and inference method. The method comprises: a data pre-processing process, an extended finite state machine (EFSM) model building process, a variable prediction process based on a feedback differential evaluation optimized extreme learning machine (FDE-ELM), and a fault inference process based on the EFSM. The method is advantaged by high model building stability, high fault prediction precision, low algorithm complexity, automatic fault inferring, and visual inference process. The method provides help for ensuring security of process industry, ensuring property and personnel safety, and saving hardware cost.

Description

technical field [0001] The present invention takes the nonlinear process industrial system as the object, aims at improving the modeling stability and fault prediction accuracy, and proposes a complex process industrial data preprocessing technology, Feedback Differential Evolution Optimized Extreme Learning Machine (Feedback Differential Evolution Optimized Extreme Learning Machine, FDE-ELM) technology, and the industrial fault prediction method of extended finite state machine (Extended Finite State Machine, EFSM) technology. Background technique [0002] The process industry, including petrochemical, metallurgy, and papermaking, is the basic industry of our country, and it involves all aspects of national life. The process industry has the characteristics of large production scale, complex process, nonlinear process and high risk factor, so it has become an important research field of fault prediction and diagnosis. In recent years, there have been frequent accidents in ...

AI Technical Summary

Problems solved by technology

However, the traditional EFSM cannot be directly introduced into the process industry, and there are still some unsolvable problems: First, the traditional EFSM data dependency graph is built entirely by mathematical models, but in the process industry system, the mathematical models are often very complicated and cannot be accurately expressed; secondly , variables between process industry systems often have time delays, traditional EFSM does not take this time delay into account, which will lead to inaccurate analysis of the relationship between variables

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