A New Hybrid Fault Prognosis Method for MFS Systems Based on Distributed Neural Networks and Recursive Bayesian Algorithm

This article introduces a new hybrid prognosis method to predict a remaining useful lifetime (RUL) of multi-functional spoiler (MFS) systems. The MFS is vital to the healthy operation of aircraft spoiler control systems, and any fault or failure in these systems could compromise the safe operation o...

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Published in:	IEEE systems journal Vol. 14; no. 4; pp. 5407 - 5416
Main Authors:	Kordestani, Mojtaba, Samadi, M. Foad, Saif, Mehrdad
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.12.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Aircraft Aircraft control Algorithms Atmospheric modeling Bayes methods Bayesian analysis Degradation model Failure Fault diagnosis fault prognosis Neural networks Parameter estimation Prognosis Prognostics and health management remaining useful life Uncertainty
ISSN:	1932-8184, 1937-9234
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Abstract	This article introduces a new hybrid prognosis method to predict a remaining useful lifetime (RUL) of multi-functional spoiler (MFS) systems. The MFS is vital to the healthy operation of aircraft spoiler control systems, and any fault or failure in these systems could compromise the safe operation of the aircraft. The proposed prognosis methodology is a hybrid framework composed of a failure parameter estimation unit and an RUL unit. The failure parameter estimation unit observes the failure parameters using distributed neural networks via available measurements of the MFS system. Simultaneously, the remaining useful life is anticipated by the RUL unit employing the estimated failure parameter with a recursive Bayesian algorithm. Moreover, a relative accuracy (RA) measure is invoked to validate the effectiveness of the proposed method. Simulink model of the MFS system is verified by experimental data of the LJ200 series aircraft under fight condition. Furthermore, simulation test results indicate a high accuracy of the distributed structure compared to a centralized network.
AbstractList	This article introduces a new hybrid prognosis method to predict a remaining useful lifetime (RUL) of multi-functional spoiler (MFS) systems. The MFS is vital to the healthy operation of aircraft spoiler control systems, and any fault or failure in these systems could compromise the safe operation of the aircraft. The proposed prognosis methodology is a hybrid framework composed of a failure parameter estimation unit and an RUL unit. The failure parameter estimation unit observes the failure parameters using distributed neural networks via available measurements of the MFS system. Simultaneously, the remaining useful life is anticipated by the RUL unit employing the estimated failure parameter with a recursive Bayesian algorithm. Moreover, a relative accuracy (RA) measure is invoked to validate the effectiveness of the proposed method. Simulink model of the MFS system is verified by experimental data of the LJ200 series aircraft under fight condition. Furthermore, simulation test results indicate a high accuracy of the distributed structure compared to a centralized network.
Author	Kordestani, Mojtaba Saif, Mehrdad Samadi, M. Foad
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SubjectTerms	Aircraft Aircraft control Algorithms Atmospheric modeling Bayes methods Bayesian analysis Degradation model Failure Fault diagnosis fault prognosis Neural networks Parameter estimation Prognosis Prognostics and health management remaining useful life Uncertainty
Title	A New Hybrid Fault Prognosis Method for MFS Systems Based on Distributed Neural Networks and Recursive Bayesian Algorithm
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