Online Anomaly Detection with Streaming Data based on Fine-grained Feature Forecasting

In the industrial applications like fault diagnosis and health management, monitoring data generally reaches sequentially in a streaming form. To recognize fault occurrence in real time without system halt, it is necessary to improve the accuracy and stability of anomaly detection with streaming data. To solve this problem, a new online anomaly detection method with streaming data is proposed based on fine-grained feature forecasting. First, to conduct fine-grained decomposition of features, a denoising autoencoder network is run to extract multiple-dimensional deep features of online data in the initial period of normal state. Second, a forecasting model with tensor Tucker decomposition and ARIMA is conducted to predict the fluctuation trend of all feature sequences. Finally, the deviation degree between the prediction values and sequentially-arrived data is calculated, and an alarm threshold is built according to the 95% confidence interval of the maximum deviation. Then the anomalous state data can be detected in real time. This paper adopts the problem of bearing early fault online detection as an example, and run comparative experiments on the IEEE PHM Challenge 2012 bearing dataset. The results show that the proposed method has good detection accuracy and is with no false alarm, while the model training does not rely on any offline data. Then the proposed method is applicable to the problem of online anomaly detection