提出基于信念规则库的故障诊断方法，提升模型鲁棒性与准确性。
A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis
- 采用信念规则库建模，系统分析诊断模型鲁棒性
- 设计三种约束策略优化模型鲁棒性，实验验证效果
- 适用于发动机与轴承故障诊断，适合工业可靠性场景
设备运行中，故障诊断对保障生产连续性、安全性和提高效率至关重要。由于传感器数据广泛用于故障诊断，其可靠性直接影响诊断结果。本文提出一种考虑鲁棒性分析的可靠故障诊断方法，旨在解决故障诊断模型的鲁棒性评估与优化问题。首先，对信念规则库（BRB）模型进行系统的鲁棒性分析；其次，提出三种鲁棒性约束策略以优化BRB故障诊断模型的鲁棒性；最后，通过柴油机WD615和凯斯西储大学轴承数据集的故障诊断实例验证所提方法的有效性。实验表明，该方法在准确率和鲁棒性方面均有所提升。
原文摘要 · Abstract (English)
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.
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