# Probabilistic Machine Learning for Uncertainty-Aware Diagnosis of Industrial Systems

arXiv: https://arxiv.org/abs/2509.18810 · HTML: https://mlsanity.com/paper/2509.18810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks](https://mlsanity.com/paper/1502.05336/) (2015)
- [Ensemble Distribution Distillation](https://mlsanity.com/paper/1905.00076/) (2019)
- [Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors](https://mlsanity.com/paper/1603.04733/) (2016)
- [Bayesian Dark Knowledge](https://mlsanity.com/paper/1506.04416/) (2015)
- [Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server](https://mlsanity.com/paper/1512.09327/) (2015)
- [A general framework for ensemble distribution distillation](https://mlsanity.com/paper/2002.11531/) (2020)
- [Isolation and Localization of Unknown Faults Using Neural Network-Based Residuals](https://mlsanity.com/paper/1910.05626/) (2019)
- [Analysis of Numerical Integration in RNN-Based Residuals for Fault Diagnosis of Dynamic Systems](https://mlsanity.com/paper/2305.04670/) (2023)
- [The LiU-ICE Benchmark -- An Industrial Fault Diagnosis Case Study](https://mlsanity.com/paper/2408.13269/) (2024)

## Abstract (arXiv)

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.