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The traditional paradigm for developing machine prognostics usually relies ongeneralization from data acquired in experiments under controlled conditionsprior to deployment of the equipment. Detecting or predicting failures andestimating machine health in this way assumes that future field data will havea very similar distribution to the experiment data. However, many complexmachines operate under dynamic environmental conditions and are used in manydifferent ways. This makes collecting comprehensive data very challenging, andthe assumption that pre-deployment data and post-deployment data follow verysimilar distributions is unlikely to hold. Transfer Learning (TL) refers tomethods for transferring knowledge learned in one setting (the source domain)to another setting (the target domain). In this work, we present a TL methodfor predicting Remaining Useful Life (RUL) of equipment, under the assumptionthat labels are available only for the source domain and not the target domain.This setting corresponds to generalizing from a limited number ofrun-to-failure experiments performed prior to deployment into makingprognostics with data coming from deployed equipment that is being used undermultiple new operating conditions and experiencing previously unseen faults. Weemploy a deviation detection method, Consensus Self-Organizing Models (COSMO),to create transferable features for building the RUL regression model. Thesefeatures capture how different target equipment is in comparison to its peers.The efficiency of the proposed TL method is demonstrated using the NASATurbofan Engine Degradation Simulation Data Set. Models using the COSMOtransferable features show better performance than other methods on predictingRUL when the target domain is more complex than the source domain.

Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models