A clustering approach to detect faults with multi-component degradations in aircraft fuel systems

Date

2020-12-18

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Department

Type

Article

ISSN

2405-8963

Format

Free to read from

Citation

Zaporowska A, Liu H, Skaf Z, Zhao Y. (2020) A clustering approach to detect faults with multi-component degradations in aircraft fuel systems. IFAC-PapersOnLine, Volume 53, Issue 3, pp. 113-118

Abstract

Accurate fault diagnosis and prognosis can significantly increase the safety and reliability of engineering systems and also reduce the maintenance costs. There is very limited relative research reported on the fault diagnosis of a complex system with multi-component degradation. The Complex Systems (CS) problem, which features multiple components simultaneously and nonlinearly interacting with each other and corresponding environment on multiple levels, has become an essential challenge in system engineering. In CS, even a single component degradation could cause misidentification of the fault severity level and lead to serious consequences. This paper introduces a new test rig to simulate multi-component degradations of the aircraft fuel system. A data analysis approach based on machine learning classification of both the time and frequency domain features is then proposed to detect and identify the fault severity level of CS with multi-component degradation. Results show that a) the fault can be sensitively detected with an accuracy > 99%; b) the severity of fault can be identified with an accuracy of 100%.

Description

Software Description

Software Language

Github

Keywords

Fast Fourier Transform, Clustering analysis, K-means clustering, Fault Diagnosis

DOI

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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