# Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks

arXiv: https://arxiv.org/abs/2409.08309 · HTML: https://mlsanity.com/paper/2409.08309/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Automatic Differentiation Variational Inference](https://mlsanity.com/paper/1603.00788/) (2016)

## Abstract (arXiv)

Fault monitoring and diagnostics are important to ensure reliability of electric motors. Efficient algorithms for fault detection improve reliability, yet development of cost-effective and reliable classifiers for diagnostics of equipment is challenging, in particular due to unavailability of well-balanced datasets, with signals from properly functioning equipment and those from faulty equipment. Thus, we propose to use a Bayesian neural network to detect and classify faults in electric motors, given its efficacy with imbalanced training data. The performance of the proposed network is demonstrated on real life signals, and a robustness analysis of the proposed solution is provided.