Collaborative Contextual Anomaly Detection for Industrial Equipment Groups

It is challenging to evaluate the degradation of equipment using data-driven approach, especially for those nonlinear industrial systems working in dynamic environment. In this work, we propose a collaborative contextual anomaly detection method that clusters similar devices with same context into collaborative groups, builds degradation profile for each device, then detects anomalies by comparing the profiles in each groups.