  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.
