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In order for a machine to operate safely, it is one of the very important factors to quickly and accurately detect an abnormality. However, it is difficult to detect accurately and always monitor the machine by human decision. Daily inspections of machines are generally performed by monitoring images, vibrations, temperature and pressure. However, it is commonly thought that inspections using sound is difficult because sound is different how to hear depending on the person. In addition, machine learning came to be used easily on these days. In this study, we examined whether machine abnormalities can be detected using machine learning. To prepare for the study, the abnormal machine sounds were needed. However, it is difficult to collect the abnormal machine sound. In this study, to generate the abnormal sound easily, we build a machine operation sound generator. Some abnormal sounds were generated by applying a load to this device. We extracted features called Mel-Frequency Cepstrum Coefficient(MFCC) from machine operation sounds. MFCC is one of the features and is popular as a method of sound recognition. Subsequently, abnormal sounds were detected by using One Class Support Vector Machine (OCSVM). OCSVM is a method to regard normal data as one class and detect other than normal data. The one of the merits of OCSVM can detect abnormal data by using results of learning only normal data. We conducted experiments to confirm the effectiveness of the proposed system. We assumed that the sounds of first day were normal sounds and the other sounds assumed abnormal sounds. As a result, the abnormality rate on the fourth day was 78.94% and we conclude that abnormal sounds could be detected from machine operation sounds.

Proposal of Abnormal Sound Detection System using OCSVM