Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer

In industrial applications, anomaly detectors are trained to raise alarms when measured samples deviate from the training data distribution. The samples used to train the model should, therefore, be sufficient in quantity and representative of all healthy operating conditions. However, for systems subject to changing operating conditions, acquiring such comprehensive datasets requires a long collection period. To train more robust anomaly detectors, we propose a new framework to perform unsupervised transfer learning (UTL) for one-class classification problems. It differs, thereby, from other applications of UTL in the literature which usually aim at finding a common structure between the datasets to perform either clustering or dimensionality reduction. The task of transferring and combining complementary training data in a completely unsupervised way has not been studied yet. The proposed methodology detects anomalies in operating conditions only experienced by other units in a fleet. We propose the use of adversarial deep learning to ensure the alignment of the different units’ distributions and introduce a new loss, inspired by a dimensionality reduction tool, to enforce the conservation of the inherent variability of each dataset. We use a state-of-the-art once-class approach to detect the anomalies. We demonstrate the benefit of the proposed framework using three open source datasets.