Symbolic Dynamic Filtering for Data-Driven Pattern Recognition. Chapter 2

Gradual development of anomalies (i.e., deviations from the nominal condition) may alter the quasi-static behavior patterns of human-engineered complex systems. This chapter presents a recently reported technique of pattern recognition, called Sym-bolic Dynamic Filtering (SDF), for early detection and prognosis of such changes in behavior patterns due to slowly evolving anomalies that may be benign or malignant. The underlying concept of SDF is built upon the principles of Symbolic Dynamics, Information Theory, and Statistical Signal Processing, where time series data from selected sensor(s) in the fast time scale of the process dynamics are analyzed at dis-crete epochs in the slow time scale of anomaly evolution. The key idea here is early detection and identification of (possible) changes in quasi-static statistical patterns of the dynamical system behavior. An important feature of this pattern recognition tech-nique is extraction of the relevant statistics by conversion of the time series data into a symbol sequence by appropriate coarse-graining of the imbedded information. As an alternative to the currently practiced method of phase-space partitioning in the tim