UDDT: An Unsupervised Drift Detection Method for Industrial Time Series Data

Industrial ML models are primarily data-driven. Therefore, one of the main focus for monitoring the model should be towards identifying the drifts in the data that might affect the performance of the model. The traditional drift detecting methods are usually based on some assumptions related to the underlying data such as no inter-dependence. However industrial sensor data typically consists of time series data, which is collected at regular intervals. Therefore, detecting drift in dependent data where the current readings depend on the previously registered readings demands a different approach. Existing solutions require either the ground truth, a fixed size, or the underlying model details. We propose an Unsupervised Drift Detection method for industrial Time series data or UDDT, a generic approach with no such pre-requisites. In our approach, we can check whether two series belong to the same model. Apart from detecting the drift in the two series, it can also provide the rationale behind the observed drift, i.e., whether the drift is due to a difference in stationarity, correlation structures, or noise distributions. We evaluate the UDDT on two datasets to demonstrate its correctness and the trust regions under various circumstances. We also establish its applicability for the real industrial setting.