A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis
Summary
The paper proposes a reliable fault diagnosis method using a belief rule base with robustness analysis, addressing sensor reliability issues, and validates the approach on WD615 diesel engine and bearing datasets.
View Cached Full Text
Cached at: 06/10/26, 06:15 AM
# A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis Source: [https://arxiv.org/abs/2606.10500](https://arxiv.org/abs/2606.10500) [View PDF](https://arxiv.org/pdf/2606.10500) > Abstract:In equipment operation, the implementation of fault diagnosis is essential to ensure the continuity and safety of production equipment, improve operational efficiency and reduce maintenance costs\. Since sensor readings are widely used for fault diagnosis, their reliability directly affects the results of fault diagnosis\. A new fault diagnosis method is proposed to address the two problems of robustness assessment and robustness optimization of fault diagnosis models\. For this purpose, a reliable fault diagnosis method based on a belief rule base \(BRB\) considering robustness analysis is proposed\. Firstly, the robustness analysis of the BRB model is carried out systematically\. Secondly, three robustness constraint strategies are proposed to optimize the robustness of the BRB fault diagnosis model\. Finally, the effectiveness of the proposed model is verified by taking the fault diagnosis of WD615 diesel engine and Case Western Reserve University bearings as an example, and the experiments show that the proposed model improves both accuracy and robustness\. ## Submission history From: Wu Zongzong \[[view email](https://arxiv.org/show-email/5cadef19/2606.10500)\] **\[v1\]**Tue, 9 Jun 2026 07:24:37 UTC \(1,059 KB\)
Similar Articles
Physically Verifiable Evidence and LLM-Based Reporting for Bearing Fault Diagnosis
This paper introduces the Diagnostic Evidence Network (DENet), a multi-task framework that extends AI-based bearing fault diagnosis to produce physically verifiable evidence, such as predicted characteristic frequencies and temporal localization of impulses, while using a QLoRA-adapted language model to generate constrained diagnostic reports that reduce hallucinated content.
BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop
The paper presents BearingNAS, a hardware-aware neural architecture search framework that designs intelligent fault diagnosis systems for bearings, targeting microcontrollers and sensor processing units with extremely limited memory (4-8 KiB RAM, 16-32 KiB Flash) while running entirely on a laptop CPU and achieving 99.50% diagnostic accuracy.
DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules
This paper introduces DiagnosticIQ, a benchmark for evaluating LLMs in translating industrial symbolic maintenance rules into actionable steps. It highlights that while frontier models perform well on standard tasks, they exhibit brittleness and pattern-matching behaviors under structural perturbations.
Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making
The paper proposes RATTL, a framework that adjusts an agent's caution based on its Bayesian belief uncertainty using Wasserstein distance for safe sequential decision making, applicable to LLM-based systems.
Label-free Industrial Fault Detection via Adversarial Inverse Reinforcement Learning: A System for Run-to-Failure Prognostics
The paper proposes an adversarial inverse reinforcement learning framework for machinery fault detection that learns a health reward from normal operational data without requiring fault labels, achieving consistent detection across multiple benchmarks.