Cascade fault diagnosis method based on root cause intensity power set belief rule base

By constructing a root cause strength power set confidence rule base, the problem that traditional methods cannot express the fault propagation chain and locate the root cause in cascading fault diagnosis is solved, and rapid and accurate fault diagnosis and root cause location of complex systems are realized.

CN121501554APending Publication Date: 2026-02-10GUILIN UNIV OF ELECTRONIC TECH
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Patent Information

Application Number
CN202511753729.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-26
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

Existing fault diagnosis methods are unable to quickly and accurately diagnose cascading faults in complex systems, especially in terms of effectively expressing fault propagation chains and locating root causes. Traditional belief rule bases lack causal analysis capabilities when faced with cascading faults.

Method used

A root cause strength power set confidence rule base is constructed. By introducing root cause strength parameters and the power set confidence rule base, the explicit expression of the fault propagation chain and root cause localization are realized. Evidence theory is used for reasoning fusion to calculate the root cause strength score of the fault.

Benefits of technology

It enables explicit expression and diagnosis of cascading fault causal chains, provides structured root cause localization capabilities, improves the robustness and adaptability of the model in complex scenarios, and overcomes the limitations of traditional methods.

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Abstract

The invention discloses a cascade fault diagnosis method based on a root cause intensity power set belief rule base. According to the technical scheme, the method comprises the steps of 1, constructing a power set belief rule base; the conclusion hypothesis unit can clearly describe a single-point fault state, a composite fault state and a causal chain fault state. And 2, introducing a root cause strength parameter: introducing the root cause strength parameter into the conclusion hypothesis unit constructed in the step 1 for quantifying the causal influence capability of the fault state as the root cause. And step 3, reasoning fusion: calculating the matching degree and the activation degree of each rule, and applying an evidence theory-based reasoning fusion algorithm to an output result to obtain power set confidence distribution of each fault hypothesis. And step 4, fault diagnosis and fault root cause positioning are carried out, and a main fault root cause is output, and the main fault root cause is the root cause with the maximum root cause strength. The method is mainly used for realizing accurate diagnosis of cascade faults in a complex system by popularizing a traditional BRB model to power set reasoning and introducing a root cause strength concept.
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