Detection of defective embedded bearings by sound analysis: a machine learning approach

This paper describes a machine learning solution for the detection of defective embedded bearings in home appliances by sound analysis. The bearings are installed deep into the home appliances at the beginning of the production process and cannot be physically accessed once they are fully assembled. Before a home appliance is put to sale, it is turned on and passed through a sound-based sensor that produces an acoustic signal. Home appliances with defective embedded bearings are detected by analyzing such signals. The approached task is very challenging, mainly because there is a small number of sample signals and the noise level in the measurements is quite high. In fact, it is showed that the signal-to-noise ratio is high enough to mask important components when applying traditional Fourier decomposition techniques. Hence, a different approach is needed. Experimental results are reported on both laboratory and production line signals. Despite the difficulty of the task, these results are encouraging. Several classification methods were evaluated and most of them achieved acceptable performance. An interesting finding is that, among the classifiers that showed better performance, some methods are highly intuitive and easy to implement. These methods are generally preferred in industry. The proposed solution is being implemented by the company which motivated this study.