An engine health management (EHM) system typically consists of automated logic for data acquisition, parameter calculation, anomaly detection and eventually, fault identification (or isolation). Accurate fault isolation is pivotal to timely and cost-effective maintenance but is often challenging due to limited fault symptom observability and the intricacy of reasoning with heterogeneous parameters. Traditional fault isolation methods often utilize a single fault isolator (SFI) that primarily relies on gas path performance parameters. While effective for many performance-related faults, such approaches often suffer from ambiguity when two or more faults have signatures that are very similar when monitored by a rather limited number of gas path sensors. In these cases, the ambiguity often has to be resolved by experienced analysts using additional information that takes many different forms, such as various nongas path symptoms, full authority digital engine control fault codes, comparisons with the companion engine, maintenance records, and quite often, the analyst's gas turbine domain knowledge. This paper introduces an intelligent reasoner that combines the strength of an optimal, physics-based SFI and a fuzzy expert system that mimics the analytical process of human experts for ambiguity resolution. A prototype diagnostic reasoner software has been developed and evaluated using existing flight data. Significant performance improvements were observed as compared with traditional SFI results. As a generic reasoning framework, this approach can be applied not only to traditional snapshot data, but to full flight data analytics as well.
Skip Nav Destination
Article navigation
April 2019
Research-Article
Intelligent Reasoning for Gas Turbine Fault Isolation and Ambiguity Resolution
Allan J. Volponi
Allan J. Volponi
Search for other works by this author on:
Liang Tang
Allan J. Volponi
Manuscript received June 22, 2018; final manuscript received June 29, 2018; published online December 4, 2018. Editor: Jerzy T. Sawicki.
J. Eng. Gas Turbines Power. Apr 2019, 141(4): 041023 (9 pages)
Published Online: December 4, 2018
Article history
Received:
June 22, 2018
Revised:
June 29, 2018
Citation
Tang, L., and Volponi, A. J. (December 4, 2018). "Intelligent Reasoning for Gas Turbine Fault Isolation and Ambiguity Resolution." ASME. J. Eng. Gas Turbines Power. April 2019; 141(4): 041023. https://doi.org/10.1115/1.4040899
Download citation file:
Get Email Alerts
Cited By
Accelerating Chemical Kinetics Calculations with Physics Informed Neural Networks
J. Eng. Gas Turbines Power
Fully Coupled Analysis of Flutter Induced Limit Cycles: Frequency Versus Time Domain Methods
J. Eng. Gas Turbines Power (July 2023)
Impact of Ignition Assistant on Combustion of Cetane 30 and 35 Jet-Fuel Blends in a Compression-Ignition Engine at Moderate Load and Speed
J. Eng. Gas Turbines Power (July 2023)
Related Articles
Empirical Tuning of an On-Board Gas Turbine Engine Model for Real-Time Module Performance Estimation
J. Eng. Gas Turbines Power (March,2008)
LES Predictions of Noise Emissions From a Low-Bypass Ratio Military Gas Turbine Engine
J. Eng. Gas Turbines Power (April,2011)
A Sketch-Based Tool for Analyzing Vibratory Mechanical Systems
J. Mech. Des (October,2008)
Development of Gas Turbine Performance Seeking Logic
J. Eng. Power (October,1978)
Related Proceedings Papers
Related Chapters
Reassessment
Air Engines: The History, Science, and Reality of the Perfect Engine
Why use GD&T?
Geometric Dimensioning and Tolerancing: Applications, Analysis, Gauging and Measurement [per ASME Y14.5-2018]
Why Use GD&T?
Geometric Dimensioning and Tolerancing Handbook: Applications, Analysis & Measurement