Technical review demonstrates sound-based anomaly detection for mechanical fault monitoring in industrial machinery, highlighting its potential to reduce operating costs and downtime.
Predictive maintenance, utilising anomalous sound classification, demonstrates a strong potential to identify mechanical faults in industrial machinery.Instead of counting only on traditional sensors, this method uses acoustic signals generated by machines to detect potential issues.The study explores how sound-based monitoring can be used for predictive maintenance in order to reduce the cost and the downtime.This perspective combines audio data collection with machine learning techniques with the aim of identifying uncommon patterns in machine operation.Applications such as the International Space Station highlight the practical value of this technology.Even if the results are promising, certain challenges, including noise and data variability, still need to be addressed.
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Tataru Ionut (2026) studied this question.
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