用概率模型量化预测不确定性，提升工业系统故障诊断可靠性。
Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems
- 采用集成概率学习自动评估诊断置信度
- 多案例测试显示诊断指标全面改善
- 适合对误报敏感的工业故障诊断场景
深度神经网络在故障诊断中广泛应用，利用历史数据捕捉系统行为，无需高保真物理模型。然而，尽管其预测能力强，现有模型常难以评估自身置信度。这一问题在基于一致性诊断中尤为关键，因决策逻辑对误报高度敏感。为此，本文提出一种基于集成概率机器学习的诊断框架，通过量化并自动化预测不确定性，提升数据驱动的一致性诊断性能。该方法在多个案例研究中通过消融实验与对比分析验证，各项诊断指标均实现稳定提升。
原文摘要 · Abstract (English)
Deep neural networks has been increasingly applied in fault diagnostics, where it uses historical data to capture systems behavior, bypassing the need for high-fidelity physical models. However, despite their competence in prediction tasks, these models often struggle with the evaluation of their confidence. This matter is particularly important in consistency-based diagnosis where decision logic is highly sensitive to false alarms. To address this challenge, this work presents a diagnostic framework that uses ensemble probabilistic machine learning to improve diagnostic characteristics of data driven consistency based diagnosis by quantifying and automating the prediction uncertainty. The proposed method is evaluated across several case studies using both ablation and comparative analyses, showing consistent improvements across a range of diagnostic metrics.
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