Knocking Sound Detection for Acoustic Condition Monitoring in Industrial Facilities

Monitoring the health of machinery in industrial environments is critical to prevent costly downtime and production disruptions. Acoustic measurements offer a promising alternative to traditional methods like vibration analysis due to their simpler instrumentation. However, accurately detecting fault sounds amidst high background noise remains a significant challenge. Machine learning approaches, for example, require extensive datasets encompassing normal and faulty operation to learn the machine's behavior. In this letter, we propose a different approach by focusing on knocking sounds, which are typical indicators of faults in industrial machinery. We describe these fault conditions using an appropriate signal model and use a general likelihood ratio test as a detector. As demonstrated in this letter, by accurately describing the fault pattern based on a small amount of fault data, very low false positive rates can be achieved, significantly reducing the effort required to collect extensive data sets for faulty machine operation.