Adaptive Time Window Enabled Model Pool for Online Deep Anomaly Detection in IIoT

In the realm of industrial Internet of Things(IIoT), the data pattern is subject to change over time, necessitating the implementation of online anomaly detection to adapt to the change of data pattern. Given the multiple stages in the production process in IIoT, data at different times exhibit varying periodic characteristics. Existing training methods primarily use fixed time windows, which struggle to adapt to complex time patterns, leading to decreased accuracy in anomaly detection. Furthermore, the incremental update method which utilizes a single model cannot effectively capture changing data characteristics. This paper introduces an online anomaly detection architecture named Adaptive Time Window enabled Model Pool (ATWMP). The framework utilizes a reinforcement learning model to dynamically select the optimal time window for model update and anomaly detection. Within the model pool framework, anomaly detection is conducted based on model reliability, and model updates are performed according to concept drift in order to ensure accurate adaptation to changing data features. Comprehensive experiments conducted on eight concept-drifted datasets and IIoT datasets demonstrate the superiority of this proposed method compared with other advanced methods.