Multi-Sensor Prognostics using an Unsupervised Health Index based on   LSTM Encoder-Decoder
  Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs. 
Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan, Gaurangi Anand,   Lovekesh Vig, Puneet Agarwal, Gautam Shroff
cs.LG cs.AI