  With emerging smart communities, improving overall system availability is
becoming a major concern. In order to improve the reliability of the components
in a system we propose an inference model to predict Remaining Useful Life
(RUL) of those components. In this paper we work with components of backend
data servers such as hard disks, that are subject to degradation. A Deep
Long-Short Term Memory (LSTM) Network is used as the backbone of this fast,
data-driven decision framework and dynamically captures the pattern of the
incoming data. In the article, we discuss the architecture of the neural
network and describe the mechanisms to choose the various hyper-parameters.
Further, we describe the challenges faced in extracting effective training sets
from highly unorganized and class-imbalanced big data and establish methods for
online predictions with extensive data pre-processing, feature extraction and
validation through online simulation sets with unknown remaining useful lives
of the hard disks. Our algorithm performs especially well in predicting RUL
near the critical zone of a device approaching failure. With the proposed
approach we are able to predict whether a disk is going to fail in next ten
days with an average precision of 0.8435. We also show that the architecture
trained on a particular model can be used to predict RUL for devices in
different models from same manufacturer through transfer learning.
