A cost effective on-site fault diagnosis method for home appliance rotor failures

Rotating components are one of the most important machine parts used in many industrial applications. Rotating machine commonly used in homes has a washing machine, which occurs with fault frequently by periodic use. Therefore, this study aims to diagnose the washing machine cheaply and accurately by using a smartphone’s microphone. This paper proposes fault diagnosis algorithm developed using FFT, skewness, kurtosis, high pass filter (HPF), A-weighting filter, and support vector machine (SVM). The FFT transforms the time domain into the frequency domain, and skewness and kurtosis analyze unbalance degree of the data. And A-weighting filter is used to filter the data as similar to human hearing and SVM is used to construct diagnostic model. The developed algorithm compensates for the shortcomings of the existing fault diagnosis method and shows high accuracy. In addition, because of using the cheap microphone of the smartphone, it is easy to commercialize due to the low cost, and the accuracy is high enough to show the analysis result almost similar to analysis result of commercial measuring instrument. So, it can be used to diagnose only using the smartphone on the spot.