  By informing the onset of the degradation process, health status evaluation
serves as a significant preliminary step for reliable remaining useful life
(RUL) estimation of complex equipment. This paper proposes a novel temporal
dynamics learning-based model for detecting change points of individual
devices, even under variable operating conditions, and utilises the learnt
change points to improve the RUL estimation accuracy. During offline model
development, the multivariate sensor data are decomposed to learn fused
temporal correlation features that are generalisable and representative of
normal operation dynamics across multiple operating conditions. Monitoring
statistics and control limit thresholds for normal behaviour are dynamically
constructed from these learnt temporal features for the unsupervised detection
of device-level change points. The detected change points then inform the
degradation data labelling for training a long short-term memory (LSTM)-based
RUL estimation model. During online monitoring, the temporal correlation
dynamics of a query device is monitored for breach of the control limit derived
in offline training. If a change point is detected, the device's RUL is
estimated with the well-trained offline model for early preventive action.
Using C-MAPSS turbofan engines as the case study, the proposed method improved
the accuracy by 5.6\% and 7.5\% for two scenarios with six operating
conditions, when compared to existing LSTM-based RUL estimation models that do
not consider heterogeneous change points.
