Anomaly detection of motors with feature emphasis using only normal sounds

This paper proposes an anomaly detection method for sound signals observed from motors in operation without using abnormal signals. It is based on feature emphasis and effectively detects anomalies that appear in a small subset of features. To emphasize the features, the method optimally estimates the contribution rates of various features to the dissimilarity score between an observed signal and the distribution of normal signals. We report here our evaluation of the method using sound data observed from PCs and fans in operation. The evaluation demonstrates that the proposed method emphasizes a small subset of narrow frequency ranges of sounds and that it achieves an error reduction rate of up to 76%.