Abnormal Sound Detection for Rotary Parts in Noisy Environment by One-class SVM and Non-negative Matrix Factorization

Abstract

In this paper, we introduce a data-driven method for detecting abnormal sound from rotary machines, which is due to small scratches on the surface of bearings, in a manufacturing plant. Since it is difficult to obtain a sufficient amount of anomalous data beforehand, we assume only normal data are available for training a model. The challenge in our situation is the presence of high-level ambient noise, which makes detecting small anomalous sound very hard. In the proposed method, feature vectors are extracted by applying short-time Fourier transform, and one-class SVM is trained on normal data and used to discriminate normal and anomalous data. In addition, ambient noise is removed using nonnegative matrix factorization before extracting features to overcome the problem of noise superimposition and to improve the discrimination precision. We show the results of an experiment using actual sound data obtained from rotary machines with ambient noise.