Symbolic Time-Series Analysis of Gas Turbine Gas Path Electrostatic Monitoring Data

Affiliation of Author(s):民航学院

Journal:JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME

Key Words:engine health monitoring electrostatic monitoring symbolic time-series analysis

Abstract:The aero-engine gas-path electrostatic monitoring system is capable of providing early warning of impending gas-path component faults. In the presented work, a method is proposed to acquire signal sample under a specific operating condition for on-line fault detection. The symbolic time-series analysis (STSA) method is adopted for the analysis of signal sample. Advantages of the proposed method include its efficiency in numerical computations and being less sensitive to measurement noise, which is suitable for in situ engine health monitoring application. A case study is carried out on a data set acquired during a turbojet engine reliability test program. It is found that the proposed symbolic analysis techniques can be used to characterize the statistical patterns presented in the gas path electrostatic monitoring data (GPEMD) for different health conditions. The proposed anomaly measure, i.e., the relative entropy derived from the statistical patterns, is confirmed to be able to indicate the gas path components faults. Finally, the further research task and direction are discussed.

ISSN No.:0742-4795

Translation or Not:no

Date of Publication:2017-10-01

Co-author:Liu, Pengpeng,Yin, Yibing,zhf,Li, Chaoyi

Correspondence Author:Sun Jianzhong

Date of Publication:2017-10-01