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Research Papers

Intelligent Reasoning for Gas Turbine Fault Isolation and Ambiguity Resolution

[+] Author and Article Information
Liang Tang

Pratt & Whitney,
400 Main Street, MS 162-15,
East Hartford, CT 06108
e-mail: liang.tang@pw.utc.com

Allan J. Volponi

Pratt & Whitney,
400 Main Street, MS 162-15,
East Hartford, CT 06108
e-mail: allan.volponi@pw.utc.com

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 141(4), 041023 (Dec 04, 2018) (9 pages) Paper No: GTP-18-1318; doi: 10.1115/1.4040899 History: Received June 22, 2018; Revised June 29, 2018

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.

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Figures

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Fig. 1

Generic health diagnostic process

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Fig. 2

Single fault isolation based on pattern matching

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Fig. 3

Sample percentage confusion matrix (truncated)

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Fig. 4

Sample 3% adjusted confusion matrix (truncated)

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Fig. 5

Intelligent reasoning architecture

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Fig. 6

The D-Matrix structure

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Fig. 7

Fuzzy membership functions for analytic engine output

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Fig. 8

The prototype reasoner GUI

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Fig. 9

Explanation of reasoning process

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Fig. 10

Sample percentage confusion matrix (complete)

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Fig. 11

Sample 3% adjusted confusion matrix (complete)

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