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The failure of a complex and safety critical industrial asset can haveextremely high consequences. Close monitoring for early detection of abnormalsystem conditions is therefore required. Data-driven solutions to this problemhave been limited for two reasons: First, safety critical assets are designedand maintained to be highly reliable and faults are rare. Fault detection canthus not be solved with supervised learning. Second, complex industrial systemsusually have long lifetime during which they face very different operatingconditions. In the early life of the system, the collected data is probably notrepresentative of future operating conditions, making it challenging to train arobust model.  In this paper, we propose a methodology to monitor the systems in their earlylife. To do so, we enhance the training dataset with other units from a fleet,for which longer observations are available. Since each unit has its ownspecificity, we propose to extract features made independent of their origin bythree unsupervised feature alignment techniques. First, using a variationalencoder, we impose a shared probabilistic encoder/decoder for both units.Second, we introduce a new loss designed to conserve inter-point spacialrelationships between the input and the learned features. Last, we propose totrain in an adversarial manner a discriminator on the origin of the features.Once aligned, the features are fed to a one-class classifier to monitor thehealth of the system. By exploring the different combinations of the proposedalignment strategies, and by testing them on a real case study, a fleetcomposed of 112 power plants operated in different geographical locations andunder very different operating regimes, we demonstrate that this alignment isnecessary and beneficial.

Domain Adaptation for One-Class Classification: Monitoring the Health of Critical Systems Under Limited Information