Transferring Complementary Operating Conditions for Anomaly Detection
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In complex industrial systems, the number of possible fault types is uncountable, making it impossible to train supervised models covering them all. Instead, anomaly detectors are trained on healthy operating condition data and raise an alarm when the data deviate from the healthy conditions, indicating the possible occurrence of faults. Data-driven anomaly detection performance relies on a representative collection of samples of the normal (healthy) class distribution. This means that the samples used to train the model should be sufficient in number and distributed so as to empirically determine the full healthy distribution. But for industrial systems in gradually varying environments or subject to changing usage, acquiring such a comprehensive set of samples would require a long collection period and delay the point at which the anomaly detector could be trained and operational. In this paper, we propose a framework for the transfer of complementary operating conditions between different units, to train more robust anomaly detectors. The domain shift due to different units' specificities needs to be accounted for. This problem is an extension of Unsupervised Domain Adaptation to the one-class classification task. We solve the problem with adversarial deep learning and replace the traditional classification loss, unavailable in one-class problems, with a new loss inspired by a dimensionality reduction tool. This loss enforces the conservation of the inherent variability of each dataset while the adversarial architecture ensures the alignment of the distributions, hence correcting the domain shift. We demonstrate the benefit of this approach using three open source datasets.
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