Detection of Electric Motor Damage Through Analysis of Sound Signals Using Bayesian Neural Networks
This work addresses fault monitoring for electric motors, which is crucial for reliability in industrial applications, but it appears incremental as it applies an existing method (Bayesian neural networks) to a specific domain problem.
The paper tackled the challenge of detecting and classifying faults in electric motors using sound signals, proposing a Bayesian neural network to handle imbalanced datasets, and demonstrated its performance on real-life signals with a robustness analysis.
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.