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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Vydáno v:IEEE systems journal Ročník 14; číslo 4; s. 5407 - 5416
Hlavní autoři: Kordestani, Mojtaba, Samadi, M. Foad, Saif, Mehrdad
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Shrnutí: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.
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content type line 14
ISSN:1932-8184
1937-9234
DOI:10.1109/JSYST.2020.2986162