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Maturity of scientific theories has facilitated§creation of advanced technology of human-engineered§complex systems. A major challenge in these systems§is online detection of behavioral uncertainties due§to gradual evolution of anomalies (i.e., deviations§from the nominal condition). These anomalies may§alter the quasi-static behavior that causes§performance degradation and can eventually lead to§catastrophic failures. Therefore, for safe and§reliable operation, it is essential to develop robust§analytical tools for online degradation monitoring§and for generating advanced warnings of emerging§anomalies. Since it is often infeasible to achieve§the required modeling accuracy due to the presence of§i) high dimensionality, ii) non-stationarity§(possibly chaotic behavior), iii) nonlinearity, and§iv) exogenous disturbances, time series analysis of§appropriate sensor data provides one of the most§powerful tools for degradation monitoring of complex§systems. This book presents a data-driven pattern§identification methodology, built upon§multidisciplinary concepts of Symbolic Dynamics,§Automata Theory and Information Theory, with diverse§applications to complex electromechanical systems.
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