# Explainable Condition Monitoring via Probabilistic Anomaly Detection Applied to Helicopter Transmissions

arXiv: https://arxiv.org/abs/2603.08130 · HTML: https://mlsanity.com/paper/2603.08130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [Nonparametric and Online Change Detection in Multivariate Datastreams using QuantTree](https://mlsanity.com/paper/2208.14801/) (2022)
- [Bayesian dependent mixture models: A predictive comparison and survey](https://mlsanity.com/paper/2307.16298/) (2023)
- [Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models](https://mlsanity.com/paper/2401.17118/) (2024)
- [CoCoAFusE: Beyond Mixtures of Experts via Model Fusion](https://mlsanity.com/paper/2505.01105/) (2025)

## Abstract (arXiv)

We present a novel Explainable methodology for Condition Monitoring, relying on healthy data only. Since faults are rare events, we propose to focus on learning the probability distribution of healthy observations only, and detect Anomalies at runtime. This objective is achieved via the definition of probabilistic measures of deviation from nominality, which allow to detect and anticipate faults. The Bayesian perspective underpinning our approach allows us to perform Uncertainty Quantification to inform decisions. At the same time, we provide descriptive tools to enhance the interpretability of the results, supporting the deployment of the proposed strategy also in safety-critical applications. The methodology is validated experimentally on two use cases: a publicly available benchmark for Predictive Maintenance, and a real-world Helicopter Transmission dataset collected over multiple years. In both applications, the method achieves competitive detection performance with respect to state-of-the-art anomaly detection methods.