Advances and Challenges in Non-Contact Acoustic Signal-Based Bearing Fault Diagnosis: A Review.

Non‑contact acoustic fault diagnosis is becoming a key way to monitor rotating electrical machines, especially rolling‑element bearings. Unlike traditional vibration methods that need a sensor glued or bolted to the machine, acoustic techniques can listen from a distance, making them less invasive, more flexible, and cheaper to deploy. This review looks at the latest progress, the main methods, and the ongoing problems in three areas: capturing acoustic signals, removing noise, and extracting useful features to spot bearing faults. It examines several signal‑processing approaches, such as wavelet transforms, empirical mode decomposition, and deep‑learning classifiers. A big part of the discussion deals with real‑world issues that hurt diagnostic accuracy: background noise, loss

of sound energy (attenuation), and the mixing of signals from multiple sources. The paper also compares different acoustic sensor layouts and spatial‑filtering algorithms, offering a practical framework for building reliable acoustic diagnostic systems. Finally, the review acts as a step‑by‑step guide for creating simulation models and lab experiments that test acoustic emissions under various loads and speeds. This helps connect theoretical signal‑processing ideas with actual machine‑health monitoring in industry.

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