This paper examines the efficacy of a novel method for anomaly detection in mechanical systems, which makes use of a hidden Markov model, derived from the time-series data of pertinent measurement(s). The core concept of the anomaly detection method is symbolic time-series analysis that is built upon the principles of Automata Theory, Information Theory, and Pattern Recognition. The performance of this method is compared with that of other existing pattern-recognition techniques from the perspective of early detection of small fatigue cracks in ductile alloy structures. The experimental apparatus, on which the anomaly detection method is tested, is a multi-degree-of-freedom mass-beam structure excited by oscillatory motion of two electromagnetic shakers. The evolution of fatigue crack damage at one or more failure sites are detected from symbolic time-series analysis of displacement sensor signals.

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Identification of statistical patterns from observed time series of spatially distributed sensor data is critical for performance monitoring and decision making in human-engineered complex systems, such as electric power generation, petrochemical, and networked transportation. This paper presents an information-theoretic approach to identification of statistical patterns in such systems, where the main objective is to enhance structural integrity and operation reliability. The core concept of pattern identification is built upon the principles of Symbolic Dynamics, Automata Theory, and Information Theory. To this end, a symbolic time series analysis method has been formulated and experimentally validated on a special-purpose test apparatus that is designed for data acquisition and real-time analysis of fatigue damage in polycrystalline alloys.