Abstract

With the development of industrial Internet technology, more and more devices are brought into the industrial big data platform. To improve the efficiency of device maintenance, the industrial big data platform needs to monitor the abnormal data of the device. However, most of the current anomaly detection algorithms are offline and they can’t be updated in real-time. To solve this problem, this paper proposes an anomaly detection model for the industrial stream. The model realizes anomaly detection by cooperatively calling 3 σ and DBSCAN algorithm. The model has the advantages of low cost, fast speed, and easy to use. On this basis, this paper presents a real-time update strategy for this model, which further improves the accuracy of the model. The experimental results of water pump equipment monitoring data show the effectiveness of this method.

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