  With the advancement of huge data generation and data handling capability,
Machine Learning and Probabilistic modelling enables an immense opportunity to
employ predictive analytics platform in high security critical industries
namely data centers, electricity grids, utilities, airport etc. where downtime
minimization is one of the primary objectives. This paper proposes a novel,
complete architecture of an intelligent predictive analytics platform, Fault
Engine, for huge device network connected with electrical/information flow.
Three unique modules, here proposed, seamlessly integrate with available
technology stack of data handling and connect with middleware to produce online
intelligent prediction in critical failure scenarios. The Markov Failure module
predicts the severity of a failure along with survival probability of a device
at any given instances. The Root Cause Analysis model indicates probable
devices as potential root cause employing Bayesian probability assignment and
topological sort. Finally, a community detection algorithm produces correlated
clusters of device in terms of failure probability which will further narrow
down the search space of finding route cause. The whole Engine has been tested
with different size of network with simulated failure environments and shows
its potential to be scalable in real-time implementation.
