Intelligent Continuous Monitoring to Handle Data Distributional Changes for IoT Systems

Intelligent continuous monitoring of an IoT system to identify the operational changes, encompassing both normal and abnormal scenarios, with drift in sensing device is a challenging problem. It demands capability of learning continuously with multiple interventions or shifts, without forgetting past events information. However, forgetting the past learned knowledge, known as catastrophic forgetting, impacts significantly on the performance of continuous monitoring. In this work, we propose a generative neural network based model to handle various operational changes. Here, one objective is to learn continually by capturing past data distributional knowledge, while adapting new data signatures. Other objective, is to handle changes in data distribution like, to identify drifts, as well as, variations in diverse operational conditions of the system. We have experimented using vibration sensor based public real-world bearing data and performed extensive analysis incorporating synthetic drifts. Proposed method outperforms existing benchmark performances with clear separation of drift in learned representation.