Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines

Abstract

This brief paper presents a symbolic dynamicsbased method for detection of incipient faults in gas turbine engines. The underlying algorithms for fault detection and classification are built upon the recently reported work on symbolic dynamic filtering. In particular, Markov modelbased analysis of quasistationary steadystate time series is extended to analysis of transient time series during takeoff. The algorithms have been validated by simulation on the NASA Commercial Modular Aero Propulsion System Simulation (CMAPSS) transient testcase generator.