Transfer Learning Approaches for Cross-Platform Intelligent Fault Prediction

Abstract

Deep learning has pushed intelligent fault diagnosis to near-perfect accuracy on the laboratory test rigs where it is usually
trained and evaluated, yet that accuracy degrades sharply the moment a model is asked to work on a different machine, a
different operating condition, or a different platform in the same industrial fleet -- the well-known problem of domain shift.
Transfer learning and domain adaptation address this gap directly by reusing knowledge learned on a data-rich source
domain to build working diagnostic models on target machines where labeled fault data is scarce or absent. This article
synthesizes the literature on cross-platform transfer learning for intelligent fault prediction, tracing the field from its
foundations in transfer learning theory and domain-adaptation generalization bounds, through deep convolutional and
autoencoder architectures purpose-built for vibration-signal diagnostics, to subdomain-level and adversarial alignment
methods that correct the class-collapse failure of naive global distribution matching, and on to harder, more industrially
realistic settings: few-shot and meta-learning-based cross-machine transfer, open-set recognition of previously unseen fault
modes, and federated transfer learning across organizational boundaries. We give particular weight to the industrial IoT and
edge-computing literature that determines whether any of this can actually run on the constrained hardware co-located with
real equipment. Read together, the evidence supports transfer learning as a mature and repeatedly validated technique for
closing much of the cross-domain accuracy gap in fault diagnosis, while leaving multi-source fleet-scale transfer, open-set
safety, and edge-deployable accuracy as an open, still largely benchmark-rig-bound research agenda.