Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 7/2019

18-07-2018 | Original Article

Fault diagnosis based on relevance vector machine for fuel regulator of aircraft engine

Authors: Jun Zhou, Yuan Liu, Tianhong Zhang

Published in: International Journal of Machine Learning and Cybernetics | Issue 7/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Fuel regulator is one of the most important components in aircraft engine fuel system and its reliability has great impact on flight safety. Fault diagnosis techniques have been proven as a good solution to improving and guaranteeing the reliability of critical unit. In this paper a fault diagnosis approach for fuel regulator of aircraft engine is developed. In the fault diagnosis strategy, a fuel regulator model is used to predict fuel flow according to engine controller command, an engine inverse model is built to precisely estimate the fuel flowing through the regulator, and the deviations among the regulator model output, engine inverse model output and feedback sensor measurement are used to detect and isolate regulator faults. Due to engine structure complexities and strong nonlinearity, it is very difficult to build exact mathematical engine inverse model. To tackle this problem, an emerging machine leaning technique, relevance vector machine, is adopted to construct the relationship between sensor readings and fuel consumption. Moreover, the proposed method is assessed by hardware-in-the-loop simulation test on an experiment platform. The experimental results verify the satisfying estimation accuracy of RVM-based engine inverse model and show that the developed fault diagnosis method for fuel regulator is effective and feasible, which is promising for fuel system reliability enhancement and aero-engine condition based maintenance.
previous article Research on classification method of high-dimensional class-imbalanced datasets based on SVM
next article A voice activity detection algorithm in spectro-temporal domain using sparse representation

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Literature
1.
2.
Martucci A (1998) Fault detection and accommodation in real time embedded full authority digital electronic engine controls. In: ASME 1998 international gas turbine and aeroengine congress and exhibition, 1998. American Society of Mechanical Engineers, pp V005T015A011–V005T015A011
3.
Kobayashi T, Simon DL (2003) Application of a bank of Kalman filters for aircraft engine fault diagnostics. In: ASME Turbo Expo 2003, collocated with the 2003 international joint power generation conference, 2003. American Society of Mechanical Engineers, pp 461–470
4.
Simon DL, Armstrong JB (2013) An integrated approach for aircraft engine performance estimation and fault diagnostics. J Eng Gas Turbines Power 135(7):071203CrossRef
5.
Tayarani-Bathaie SS, Khorasani K (2015) Fault detection and isolation of gas turbine engines using a bank of neural networks. J Process Control 36:22–41CrossRef
6.
Volponi AJ, DePold H, Ganguli R, Daguang C (2003) The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. J Eng Gas Turbines Power 125(4):917–924CrossRef
7.
Lee S-M, Roh T-S, Choi D-W (2009) Defect diagnostics of SUAV gas turbine engine using hybrid SVM-artificial neural network method. J Mech Sci Technol 23(2):559–568CrossRef
8.
Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—Part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans Industr Electron 62(6):3757–3767CrossRef
9.
Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—Part II: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans Industr Electron 62(6):3768–3774
10.
Vong CM, Wong PK, Wong KI (2014) Simultaneous-fault detection based on qualitative symptom descriptions for automotive engine diagnosis. Appl Soft Comput 22:238–248CrossRef
11.
Wong PK, Zhong J, Yang Z, Vong CM (2016) Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis. Neurocomputing 174:331–343CrossRef
12.
Kobayashi T, Simon DL (2007) Hybrid kalman filter approach for aircraft engine in-flight diagnostics: sensor fault detection case. Journal of Eng Gas Turbines Power 129(3):746–754CrossRef
13.
Rahme S, Meskin N (2015) Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine. Control Eng Pract 38:57–74CrossRef
14.
Yu Y, Li W, Sheng D, Chen J (2015) A novel sensor fault diagnosis method based on modified ensemble empirical mode decomposition and probabilistic neural network. Measurement 68:328–336CrossRef
15.
Yu B, Liu D, Zhang T (2011) Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy. Sensors 11(10):9928–9941CrossRef
16.
Chen J, Wang Y, Weng S (2010) Research on fault detection and identification of gas turbines sensors based on wavelet entropy. J Energy Inst 83(4):202–209CrossRef
17.
Aretakis N, Mathioudakis K, Stamatis A (2004) Identification of sensor faults on turbofan engines using pattern recognition techniques. Control Eng Pract 12(7):827–836CrossRef
18.
Ogaji S, Singh R, Probert S (2002) Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine. Appl Energy 71(4):321–339CrossRef
19.
Zhao Y, Sun J (2010) Fast online approximation for hard support vector regression and its application to analytical redundancy for aeroengines. Chin J Aeronaut 23(2):145–152CrossRef
20.
Sampath S, Singh R (2006) An integrated fault diagnostics model using genetic algorithm and neural networks. J Eng Gas Turbines Power 128(1):49–56CrossRef
21.
22.
Kong C, Lim S (2011) Study on fault diagnostics of a turboprop engine using inverse performance model and artificial intelligent methods. In: ASME 2011 Turbo Expo: turbine technical conference and exposition, 2011. American Society of Mechanical Engineers, pp 75–83
23.
Mahmood S, Griffin I, Fleming P (2001) Application of the Rolls Royce inverse model to trent aero engines. Mahmood S, MSc in Control Systems
24.
Lichtsinder M, Levy Y (2005) Jet engine component and transducer fault diagnosis using an inverse dynamic model. In: ASME turbo expo 2005: power for land, sea, and air, 2005. American Society of Mechanical Engineers, pp 571–582
25.
Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244MathSciNetMATH
26.
Zio E, Di Maio F (2012) Fatigue crack growth estimation by relevance vector machine. Expert Syst Appl 39(12):10681–10692CrossRef
27.
Saha B, Goebel K, Poll S, Christophersen J (2009) Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Trans Instrum Meas 58(2):291–296CrossRef
28.
Wong KI, Wong PK, Cheung CS (2015) Modelling and prediction of diesel engine performance using relevance vector machine. Int J Green Energy 12(3):265–271CrossRef
29.
Hu L, Hu NQ, Fan B, Gu FS, Zhang XY (2015) Modeling the relationship between vibration features and condition parameters using relevance vector machines for health monitoring of rolling element bearings under varying operation conditions. Math Probl Eng. https://​doi.​org/​10.​1155/​2015/​123730 CrossRef
30.
Fei H, Jinwu X, Min L, Jianhong Y (2013) Product quality modelling and prediction based on wavelet relevance vector machines. Chemometr Intell Lab Syst 121:33–41CrossRef
31.
Cal-Braz JA, Matos LJ, Cataldo E (2013) The relevance vector machine applied to the modeling of wireless channels. IEEE Trans Antennas Propagation 61(12):6157–6167CrossRef
32.
Kim J, Suga Y, Won S (2006) A new approach to fuzzy modeling of nonlinear dynamic systems with noise: relevance vector learning mechanism. IEEE Trans Fuzzy Syst 14(2):222–231CrossRef
33.
Fathy HK, Filipi ZS, Hagena J, Stein JL (2006) Review of hardware-in-the-loop simulation and its prospects in the automotive area. In: Defense and Security Symposium, 2006. International Society for Optics and Photonics, pp 62280E–62280E–62220
34.
Rankin DJ, Jiang J (2011) A hardware-in-the-loop simulation platform for the verification and validation of safety control systems. IEEE Trans Nuclear Sci 58(2):468–478CrossRef
35.
Isermann R, Schaffnit J, Sinsel S (1999) Hardware-in-the-loop simulation for the design and testing of engine-control systems. Control Eng Pract 7(5):643–653CrossRef
36.
Zhang H, Su M, Weng S (2005) Hardware-in-the-loop simulation study on the fuel control strategy of a gas turbine engine. J Eng Gas Turbines Power 127(3):693–695CrossRef
37.
Montazeri-Gh M, Nasiri M, Jafari S (2011) Real-time multi-rate HIL simulation platform for evaluation of a jet engine fuel controller. Simul Model Pract Theory 19(3):996–1006CrossRef
38.
Montazeri-Gh M, Nasiri M, Rajabi M, Jamshidfard M (2012) Actuator-based hardware-in-the-loop testing of a jet engine fuel control unit in flight conditions. Simul Model Pract Theory 21(1):65–77CrossRef
39.
Montazeri-Gh M, Nasiri M (2013) Hardware-in-the-loop simulation for testing of electro-hydraulic fuel control unit in a jet engine application. Simulation 89(2):225–233CrossRef
40.
Hashemi S, Montazeri M, Nasiri M (2014) The compensation of actuator delay for hardware-in-the-loop simulation of a jet engine fuel control unit. Simulation 90:0037549714534301CrossRef
41.
Lu F, Huang J, Xing Y (2012) Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model. Sensors 12(8):11061–11076CrossRef
Metadata
Title
Fault diagnosis based on relevance vector machine for fuel regulator of aircraft engine
Authors
Jun Zhou
Yuan Liu
Tianhong Zhang
Publication date
18-07-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 7/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0855-0

Other articles of this Issue 7/2019

International Journal of Machine Learning and Cybernetics 7/2019 Go to the issue

Original Article

F-WSS: incremental wrapper subset selection algorithm for fuzzy extreme learning machine

Original Article

A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares

Correction

Correction to: An end-to-end differential network learning method for semantic segmentation

Original Article

Joint sparse representation and locality preserving projection for feature extraction

Original Article

Local dense mixed region cutting + global rebalancing: a method for imbalanced text sentiment classification

Original Article

A voice activity detection algorithm in spectro-temporal domain using sparse representation