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This paper addresses the problem of multiple parameter estimation in dynamical systems, where the solution algorithm is built upon the principles of extracting statistical information contents or patterns in the framework of symbolic domain filtering. The proposed algorithm has been tested for estimation of two slowly varying parameters in an active electronic system that is constructed in the classical...
This paper presents real-time detection of fatigue damage in mechanical structures using ultrasonic sensing methodology. The data-driven pattern identification method for anomaly detection is based on the tools derived from statistical mechanics and symbolic dynamics. The concept of escort distributions has been used to identify the behavioral patterns changes in complex systems due to gradual evolution...
Symbolic dynamic filtering (SDF) has been reported in recent literature for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. In this context, instead of solely relying on physics- based modeling that may be difficult to formulate and validate, this paper proposes data-driven modeling and system identification based on the concept of symbolic dynamics,...
This paper formulates and validates a novel methodology for diagnosis and isolation of incipient faults in aircraft gas turbine engines. In addition to abrupt large faults, the proposed method is capable of detecting and isolating slowly evolving anomalies (i.e., deviations from the nominal behavior), based on analysis of time series data observed from the instrumentation in engine components. The...
Symbolic Dynamic Filtering (SDF) has been recently reported in literature as a pattern recognition tool for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. This paper presents a comparative evaluation of SDF relative to other classes of pattern recognition tools, such as Bayesian Filters and Artificial Neural Networks, from the perspectives of:...
This paper presents the formulation and evaluation of effective algorithms of reliable data analysis for real-time monitoring of incipient faults and anomalies, data fusion and event classification. The objective is to alleviate the shortcomings of the existing techniques for data mining by taking advantage of nonlinear filtering to handle non-Gaussian and non-stationary multiplicative noise and uncertainties...
A novel path planning algorithm L* is introduced that reduces the problem to optimization of a probabilistic finite state machine and applies the rigorous theory of language-measure-theoretic optimal control to compute v-optimal paths to the specified goal. It is shown that although the underlying navigation model is probabilistic, the proposed algorithm computes plans that can be executed in a deterministic...
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