A health management decision-making method suitable for complex process systems

Abstract

The invention discloses a health management decision-making method suitable for a complex process system. The method comprises the steps of 1, acquiring a system measuring point parameter; 2, pre-processing the system measuring point parameter; 3, performing real-time fault detection on the pre-processed system measuring point parameter through an adaptive threshold analysis method, and performing fault diagnosis on the fault detection result by using a knowledge reasoning method in combination with historical data, a fault mode and an influence analysis table; 4, evaluating the health degrees of single-parameter sensors by combining the fault diagnosis result and using a grey theory method, and fusing the health degrees of the single-parameter sensors by using a fuzzy set fusion theory to obtain a fault mode health parameter; 5, predicting a fault mode health parameter by using the obtained fault mode health parameter through a relevance vector machine method; and 6, fusing maintenance decisions of multiple decision theories by using a grey group decision-making theory to obtain a maintenance decision result. The method can be used for fault prediction for next operation of the system, and provides a maintenance advice for each fault mode.

Description

Technical field [0001] The invention relates to the field of health management decision-making for complex process systems, in particular to a health management decision-making method suitable for complex process systems. Background technique [0002] Complex process systems are the subject of complexity science and are widely used in many fields such as industry, manufacturing, aviation, and aerospace. How to improve the safety, reliability, availability, effectiveness and economy of complex process systems has also become more and more critical issues considered in its technological development. For example, the rocket engine test bed is an important part of the spacecraft and the vehicle propulsion system. It is a fluid-thermodynamic system with a complex structure, involving numerous parts and components, and the structure of each part is related to each other. influences. It often works in harsh environments (high temperature, high pressure, strong corrosion and high-densi...

AI Technical Summary

Problems solved by technology

[0025] However, due to the immature research on group decision-making technology, how to accurately obtain decision-making information, including decision-making elements such as attribute values, attribute weights, and decision-maker weight information, has not yet formed a complete framework system

Especially due to the complexity and uncertainty of the process system, in the actual decision-making problems, the data quality is often low or even partially missing, that is, the uncertainty of information

For the above-mentioned group decision-making problems with uncertain information and diverse data types, traditional decision-making theories such as D-S evidence theory, Bayes theory, and fuzzy set theory will face severe challenges.

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