Abnormal vibration detection in the bearing-shaft system via semi-supervised classification of accelerometer signal patterns

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Several methods have been proposed for fault detection in mechanical systems based on sensor signals. It is preferable that corresponding label for each sensor signal should be provided and analyzed via appropriate supervised classification methods. However, the label information about a system’s status often does not perfectly pair to the corresponding data. Therefore, we apply a semi-supervised classification for fault detection using pattern extraction of multivariate signals. This approach transforms continuous time series into a set of contiguous bins via multivariate discretization. Then, we identify informative patterns in the system states, by using a self-training method with limited label information. To demonstrate the effectiveness of the proposed extraction method, five accelerometer signals are collected from a bearing-shaft system. The proposed method successfully reveals informative fault patterns that can be applied as references for fault detection. The method achieved a higher detection performance, regardless the ratio of unlabeled inputs in datasets.