Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems
This work addresses the need for reliable confidence evaluation in consistency-based diagnosis for industrial systems, though it appears incremental as it builds on existing probabilistic methods.
The paper tackled the problem of uncertainty quantification in deep neural networks for fault diagnostics, presenting an ensemble probabilistic machine learning framework that improved diagnostic metrics across multiple case studies.
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.