One Class SVM を用いたベアリング異常音検知システム

抄録

In order for a machine to operate safely, it is one of the very important factors to quickly and accurately detect an abnormality. A skilled operator can make a diagnosis from the sound produced by the machine. However, constant monitoring by the operator is costly and inefficient. If abnormalities can be detected and maintained at an early stage, it will lead to avoiding serious failures, and cost reduction can be expected by long-term use of the machine. In this study, we proposed a system that uses machine learning to detect abnormalities from the operating sounds of machines. Instead of the conventional judgment method that requires abnormal sounds in advance, a system that judges them only from learning normal sound data is proposed. In order to confirm the effectiveness of the proposed system, we build a machine operation sound generator and conducted experiments by artificially generating abnormal noise of a bearing. We extracted features called Mel-Frequency Cepstrum Coefficient from the sounds, abnormal sounds were detected by using One Class Support Vector Machine. As a result, it was confirmed that the generated abnormal noise was detected, and it was found that the proposed system was effective for abnormality detection.