  Modern distributed cyber-physical systems encounter a large variety of
anomalies and in many cases, they are vulnerable to catastrophic fault
propagation scenarios due to strong connectivity among the sub-systems. In this
regard, root-cause analysis becomes highly intractable due to complex fault
propagation mechanisms in combination with diverse operating modes. This paper
presents a new data-driven framework for root-cause analysis for addressing
such issues. The framework is based on a spatiotemporal feature extraction
scheme for distributed cyber-physical systems built on the concept of symbolic
dynamics for discovering and representing causal interactions among subsystems
of a complex system. We present two 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
multi-class classification framework using deep neural networks, DNN).
Synthetic data from cases with failed pattern(s) and anomalous node are
simulated to validate the proposed approaches, then compared with the
performance of vector autoregressive (VAR) model-based root-cause analysis.
Real dataset based on Tennessee Eastman process (TEP) is also used for
validation. The results show that: (1) $S^3$ and $A^3$ approaches can obtain
high accuracy in root-cause analysis and successfully handle multiple nominal
operation modes, and (2) the proposed tool-chain is shown to be scalable while
maintaining high accuracy.
