  Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time
series data is useful to enable condition-based maintenance and ensure high
operational availability of equipment. We propose a novel deep learning based
approach for Prognostics with Uncertainty Quantification that is useful in
scenarios where: (i) access to labeled failure data is scarce due to rarity of
failures (ii) future operational conditions are unobserved and (iii) inherent
noise is present in the sensor readings. All three scenarios mentioned are
unavoidable sources of uncertainty in the RUL estimation process often
resulting in unreliable RUL estimates. To address (i), we formulate RUL
estimation as an Ordinal Regression (OR) problem, and propose LSTM-OR: deep
Long Short Term Memory (LSTM) network based approach to learn the OR function.
We show that LSTM-OR naturally allows for incorporation of censored operational
instances in training along with the failed instances, leading to more robust
learning. To address (ii), we propose a simple yet effective approach to
quantify predictive uncertainty in the RUL estimation models by training an
ensemble of LSTM-OR models. Through empirical evaluation on C-MAPSS turbofan
engine benchmark datasets, we demonstrate that LSTM-OR is significantly better
than the commonly used deep metric regression based approaches for RUL
estimation, especially when failed training instances are scarce. Further, our
uncertainty quantification approach yields high quality predictive uncertainty
estimates while also leading to improved RUL estimates compared to single best
LSTM-OR models.
