Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks
<|reference_start|>Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks: 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.<|reference_end|>
@article{bauer2024detection,
    title={Detection of Electric Motor Damage Through Analysis of Sound Signals
  Using Bayesian Neural Networks},
    author={Waldemar Bauer and Marta Zagorowska and Jerzy Baranowski},
    journal={arXiv preprint arXiv:2409.08309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08309},
    primaryClass={eess.AS cs.LG cs.SD}
}