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Performance monitoring, anomaly detection, and root-cause analysis in complexcyber-physical systems (CPSs) are often highly intractable due to widelydiverse operational modes, disparate data types, and complex fault propagationmechanisms. This paper presents a new data-driven framework for root-causeanalysis, based on a spatiotemporal graphical modeling approach built on theconcept of symbolic dynamics for discovering and representing causalinteractions among sub-systems of complex CPSs. We formulate the root-causeanalysis problem as a minimization problem via the proposed inference basedmetric and present two approximate approaches for root-cause analysis, namelythe sequential state switching ($S^3$, based on free energy concept of arestricted Boltzmann machine, RBM) and artificial anomaly association ($A^3$, aclassification framework using deep neural networks, DNN). Synthetic data fromcases with failed pattern(s) and anomalous node(s) are simulated to validatethe 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 analysisunder both pattern-based and node-based fault scenarios, in addition tosuccessfully handling multiple nominal operating modes, (2) the proposedtool-chain is shown to be scalable while maintaining high accuracy, and (3) theproposed framework is robust and adaptive in different fault conditions andperforms better in comparison with the state-of-the-art methods.

Root-cause Analysis for Time-series Anomalies via Spatiotemporal Graphical Modeling in Distributed Complex Systems