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By informing the onset of the degradation process, health status evaluationserves as a significant preliminary step for reliable remaining useful life(RUL) estimation of complex equipment. This paper proposes a novel temporaldynamics learning-based model for detecting change points of individualdevices, even under variable operating conditions, and utilises the learntchange points to improve the RUL estimation accuracy. During offline modeldevelopment, the multivariate sensor data are decomposed to learn fusedtemporal correlation features that are generalisable and representative ofnormal operation dynamics across multiple operating conditions. Monitoringstatistics and control limit thresholds for normal behaviour are dynamicallyconstructed from these learnt temporal features for the unsupervised detectionof device-level change points. The detected change points then inform thedegradation data labelling for training a long short-term memory (LSTM)-basedRUL estimation model. During online monitoring, the temporal correlationdynamics of a query device is monitored for breach of the control limit derivedin offline training. If a change point is detected, the device's RUL isestimated with the well-trained offline model for early preventive action.Using C-MAPSS turbofan engines as the case study, the proposed method improvedthe accuracy by 5.6\% and 7.5\% for two scenarios with six operatingconditions, when compared to existing LSTM-based RUL estimation models that donot consider heterogeneous change points.

A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions