Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things

Industrial sensors have presently emerged as a very important device for monitoring environmental conditions in the manufacturing system. However, abnormal behavior of these smart sensors may cause some failure or potential risk during system operation, thereby increasing the high availability of the entire manufacturing process. An anomaly detection tool in industrial monitoring system must detect any abnormal behavior in advance. Recently, self-supervised learning demonstrated comparable performance with other methods while eliminating manually labeled processes in training. Moreover, this technique decreases the complexity of the training model in lightweight devices to increase the processing time and detect accurately the health of equipment assets. Therefore, this paper proposes an anomaly detection method using a self-supervised learning framework in a time-series dataset to improve the model performance in terms of high accuracy and lightweight method. With the consideration of time-series data augmentation for generating pseudo-label, a classifier using one-dimension convolutional neural network (1DCNN) is applied to learn the characteristics of normal data. This classification model output will effectively measure the degree of abnormality. The experimental results indicate that our proposed method outperforms classic anomaly detection methods. Furthermore, the model deployment in a real testbed is performed to illustrate the efficiency of the self-supervised learning method for time-series anomaly detection.