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

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%.