  Performance monitoring, anomaly detection, and root-cause analysis in complex
cyber-physical systems (CPSs) are often highly intractable due to widely
diverse operational modes, disparate data types, and complex fault propagation
mechanisms. This paper presents a new data-driven framework for root-cause
analysis, based on a spatiotemporal graphical modeling approach built on the
concept of symbolic dynamics for discovering and representing causal
interactions among sub-systems of complex CPSs. We formulate the root-cause
analysis problem as a minimization problem via the proposed inference based
metric and present two approximate approaches for root-cause analysis, namely
the sequential state switching ($S^3$, based on free energy concept of a
restricted Boltzmann machine, RBM) and artificial anomaly association ($A^3$, a
classification framework using deep neural networks, DNN). Synthetic data from
cases with failed pattern(s) and anomalous node(s) are simulated to validate
the proposed approaches. Real dataset based on Tennessee Eastman process (TEP)
is also used for comparison with other approaches. The results show that: (1)
$S^3$ and $A^3$ approaches can obtain high accuracy in root-cause analysis
under both pattern-based and node-based fault scenarios, in addition to
successfully handling multiple nominal operating modes, (2) the proposed
tool-chain is shown to be scalable while maintaining high accuracy, and (3) the
proposed framework is robust and adaptive in different fault conditions and
performs better in comparison with the state-of-the-art methods.
