Adaptation for Automated Drift Detection in Electromechanical Machine Monitoring

Practical machine learning applications for streaming data can involve concept drift (the change in statistical properties of data over time), one-shot or few-shot learning (starting with only one or a few examples for each class), a scarcity of representative training data, and extreme verification latency (only the initial dataset has ground-truth labels). This work presents a framework for organizing signal processing and machine learning techniques to provide adaptive classification and drift detection. Nonintrusive load monitoring (NILM) serves as an ideal case study, as modern sensing solutions provide a wellspring of electromechanical data sources. There is a lack of training datasets that generalize across different load and fault scenarios. Accordingly, training must be accomplished with a limited set of data when deploying a NILM to a new power system. Also, loads can exhibit concept drift over time either due to faults or normal variation. NILM field data is used as an illustrative case study to demonstrate the proposed framework for adaptation and drift tracking.