Unsupervised and non-parametric learning-based anomaly detection system using vibration sensor data

In this paper, we propose an anomaly detection system of machines using a hybrid learning mechanism that combines two kinds of machine learning approaches, namely unsupervised and non-parametric learning. To do so, we used vibration data, which is known to be suitable for anomaly detection in machines during operation. Furthermore, in order to take into account various characteristics of abnormal data such as scarcity and diversity, we propose a novel method that can detect anomalous behaviors using normal patterns instead of abnormal patterns from the machines. That is, we first perform a machine learning of the normal patterns of the machines during operation, and if any of the operation patterns deviates from the normal pattern, we identify that pattern as abnormal. A key characteristic of our system is that it does not use any prior information such as predefined data labels or data distributions to learn the normal operation patterns. To demonstrate the superiority of our system, we constructed a test bed consisting of a washing machine and a 3-axis accelerometer. We also demonstrated that our system can improve the accuracy of anomaly detection for the machines compared to other approaches.

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