Hybrid prognostic method applied to mechatronic systems
Fault detection and isolation, or fault diagnostic, of mechatronic systems has been the subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in...
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| Published in | International journal of advanced manufacturing technology Vol. 69; no. 1-4; pp. 823 - 834 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.10.2013
Springer Nature B.V Springer Verlag |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0268-3768 1433-3015 |
| DOI | 10.1007/s00170-013-5064-0 |
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| Abstract | Fault detection and isolation, or fault diagnostic, of mechatronic systems has been the subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, diagnosing faults may not be sufficient and one needs to anticipate the fault. This is what is done by fault prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics, and hybrid prognostics. In this paper, a hybrid prognostic method is proposed and applied on a mechatronic system. The method relies on two phases: an offline phase to build the behavior and degradation models and an online phase to assess the health state of the system and predict its remaining useful life. |
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| AbstractList | Fault detection and isolation, or fault diagnostic, of mechatronic systems has been subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, diagnosing faults may not be sufficient and one needs to anticipate the fault. This is what is done by fault prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics and hybrid prognostics. In this paper, a hybrid prognostic method is proposed and applied on a mechatronic system. The method relies on two phases: an offline phase to build the behavior and degradation models and an online phase to assess the health state of the system and predict its remaining useful life. Fault detection and isolation, or fault diagnostic, of mechatronic systems has been the subject of several interesting works. Detecting and isolating faults may be convenient for some applications where the fault does not have severe consequences on humans as well as on the environment. However, in some situations, diagnosing faults may not be sufficient and one needs to anticipate the fault. This is what is done by fault prognostics. This latter activity aims at estimating the remaining useful life of systems by using three main approaches: data-driven prognostics, model-based prognostics, and hybrid prognostics. In this paper, a hybrid prognostic method is proposed and applied on a mechatronic system. The method relies on two phases: an offline phase to build the behavior and degradation models and an online phase to assess the health state of the system and predict its remaining useful life. |
| Author | Medjaher, K. Zerhouni, N. |
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| Cites_doi | 10.1016/S0967-0661(97)00046-4 10.1016/j.ymssp.2008.06.009 10.1007/s00170-009-2482-0 10.1115/1.1640638 10.1016/j.ymssp.2006.10.001 10.1109/TR.2012.2194175 10.1016/j.ymssp.2008.12.006 10.1007/s00170-004-2131-6 10.1016/j.mechatronics.2007.03.001 10.1016/j.ymssp.2011.10.018 10.1016/j.ymssp.2005.09.012 10.1016/j.ymssp.2010.11.018 10.1016/j.conengprac.2005.01.004 10.1016/j.compchemeng.2005.02.026 |
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| Copyright | Springer-Verlag London 2013 The International Journal of Advanced Manufacturing Technology is a copyright of Springer, (2013). All Rights Reserved. Distributed under a Creative Commons Attribution 4.0 International License |
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| Keywords | Fault prognostics Fault detection Fault diagnostics Remaining useful life Bond graph modeling |
| Language | English |
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| References | PengYDongMZuoMJCurrent status of machine prognostics in condition-based maintenance: a reviewInt J Adv Manuf Tech20105029731310.1007/s00170-009-2482-0 Lebold M, Thurston M (2001) Open standards for condition-based maintenance and prognostic systems. In: Proceedings 5th maintenance and reliability conference (MARCON) KarnoppDMargolisDRosenbergRSystems dynamics: modeling and simulation of mechatronic systems20062New YorkWiley HengATanACMathewJMontgomeryNBanjevicDJardineAKIntelligent condition-based prediction of machinery reliabilityMech Syst Signal Process20092351600161410.1016/j.ymssp.2008.12.006 DongMHeDA segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodologyMech Syst Signal Process2007212248226610.1016/j.ymssp.2006.10.001 MerzoukiRMedjaherKDjeziriMAOuld-BouamamaBBacklash fault detection in mechatronic systemMechatronics20071729931010.1016/j.mechatronics.2007.03.001 SamantarayAKOuld BouamamaBModel-based process supervision: a bond graph approach2008LondonSpringer Ould BouamamaBMedjaherKSamantarayAKStaroswieckiMSupervision of an industrial steam generator. Part I: bond graph modellingControl Eng Pract2006141718310.1016/j.conengprac.2005.01.004 Tobon-MejiaDAMedjaherKZerhouniNCNC machine tool’s wear diagnostic and prognostic by using dynamic bayesian networksMech Syst Signal Process20122816718210.1016/j.ymssp.2011.10.018 MedjaherKTobon-MejiaDAZerhouniNRemaining useful life estimation of critical components with application to bearingsIEEE Trans Reliab201261229230210.1109/TR.2012.2194175 AFNOR (2005) Condition monitoring and diagnostics of machines—prognostics—part 1: general guidelines. NF ISO 13381-1 IsermannRSupervision, fault-detection and fault-diagnosis methods—an introductionControl Eng Pract1997563965210.1016/S0967-0661(97)00046-4 SikorskaJHodkiewiczMMaLPrognostic modelling options for remaining useful life estimation by industryMech Syst Signal Process2011251803183610.1016/j.ymssp.2010.11.018 HengAZhangSTanACMathewJRotating machinery prognostics: state of the art, challenges and opportunitiesMech Syst Signal Process200923372473910.1016/j.ymssp.2008.06.009 KothamasuRHuangSHVerduinWHSystem health monitoring and prognostics—a review of current paradigms and practicesInt J Adv Manuf Tech2006281012102410.1007/s00170-004-2131-6 JardineAKLinDBanjevicDA review on machinery diagnostics and prognostics implementing condition-based maintenanceMech Syst Signal Process20062071483151010.1016/j.ymssp.2005.09.012 LuoJPattipatiKRQiaoLChigusaSModel-based prognostic techniques applied to a suspension systemTrans Syst Man Cybern20033811561168 VenkatasubramanianVPrognostic and diagnostic monitoring of complex systems for product lifecycle management: challenges and opportunitiesComput Chem Eng20052961253126310.1016/j.compchemeng.2005.02.026 ChelidzeDCusumanoJA dynamical systems approach to failure prognosisJ Vib Acoust20041262810.1115/1.1640638 5064_CR1 J Sikorska (5064_CR17) 2011; 25 J Luo (5064_CR11) 2003; 38 AK Jardine (5064_CR7) 2006; 20 R Merzouki (5064_CR13) 2007; 17 A Heng (5064_CR4) 2009; 23 D Karnopp (5064_CR8) 2006 R Kothamasu (5064_CR9) 2006; 28 A Heng (5064_CR5) 2009; 23 R Isermann (5064_CR6) 1997; 5 V Venkatasubramanian (5064_CR19) 2005; 29 D Chelidze (5064_CR2) 2004; 126 AK Samantaray (5064_CR16) 2008 Y Peng (5064_CR15) 2010; 50 K Medjaher (5064_CR12) 2012; 61 B Ould Bouamama (5064_CR14) 2006; 14 5064_CR10 M Dong (5064_CR3) 2007; 21 DA Tobon-Mejia (5064_CR18) 2012; 28 |
| References_xml | – reference: SamantarayAKOuld BouamamaBModel-based process supervision: a bond graph approach2008LondonSpringer – reference: ChelidzeDCusumanoJA dynamical systems approach to failure prognosisJ Vib Acoust20041262810.1115/1.1640638 – reference: IsermannRSupervision, fault-detection and fault-diagnosis methods—an introductionControl Eng Pract1997563965210.1016/S0967-0661(97)00046-4 – reference: SikorskaJHodkiewiczMMaLPrognostic modelling options for remaining useful life estimation by industryMech Syst Signal Process2011251803183610.1016/j.ymssp.2010.11.018 – reference: AFNOR (2005) Condition monitoring and diagnostics of machines—prognostics—part 1: general guidelines. NF ISO 13381-1 – reference: HengAZhangSTanACMathewJRotating machinery prognostics: state of the art, challenges and opportunitiesMech Syst Signal Process200923372473910.1016/j.ymssp.2008.06.009 – reference: KothamasuRHuangSHVerduinWHSystem health monitoring and prognostics—a review of current paradigms and practicesInt J Adv Manuf Tech2006281012102410.1007/s00170-004-2131-6 – reference: MedjaherKTobon-MejiaDAZerhouniNRemaining useful life estimation of critical components with application to bearingsIEEE Trans Reliab201261229230210.1109/TR.2012.2194175 – reference: DongMHeDA segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodologyMech Syst Signal Process2007212248226610.1016/j.ymssp.2006.10.001 – reference: VenkatasubramanianVPrognostic and diagnostic monitoring of complex systems for product lifecycle management: challenges and opportunitiesComput Chem Eng20052961253126310.1016/j.compchemeng.2005.02.026 – reference: HengATanACMathewJMontgomeryNBanjevicDJardineAKIntelligent condition-based prediction of machinery reliabilityMech Syst Signal Process20092351600161410.1016/j.ymssp.2008.12.006 – reference: KarnoppDMargolisDRosenbergRSystems dynamics: modeling and simulation of mechatronic systems20062New YorkWiley – reference: Lebold M, Thurston M (2001) Open standards for condition-based maintenance and prognostic systems. In: Proceedings 5th maintenance and reliability conference (MARCON) – reference: LuoJPattipatiKRQiaoLChigusaSModel-based prognostic techniques applied to a suspension systemTrans Syst Man Cybern20033811561168 – reference: Tobon-MejiaDAMedjaherKZerhouniNCNC machine tool’s wear diagnostic and prognostic by using dynamic bayesian networksMech Syst Signal Process20122816718210.1016/j.ymssp.2011.10.018 – reference: JardineAKLinDBanjevicDA review on machinery diagnostics and prognostics implementing condition-based maintenanceMech Syst Signal Process20062071483151010.1016/j.ymssp.2005.09.012 – reference: MerzoukiRMedjaherKDjeziriMAOuld-BouamamaBBacklash fault detection in mechatronic systemMechatronics20071729931010.1016/j.mechatronics.2007.03.001 – reference: PengYDongMZuoMJCurrent status of machine prognostics in condition-based maintenance: a reviewInt J Adv Manuf Tech20105029731310.1007/s00170-009-2482-0 – reference: Ould BouamamaBMedjaherKSamantarayAKStaroswieckiMSupervision of an industrial steam generator. Part I: bond graph modellingControl Eng Pract2006141718310.1016/j.conengprac.2005.01.004 – volume: 5 start-page: 639 year: 1997 ident: 5064_CR6 publication-title: Control Eng Pract doi: 10.1016/S0967-0661(97)00046-4 – ident: 5064_CR1 – volume-title: Model-based process supervision: a bond graph approach year: 2008 ident: 5064_CR16 – volume: 23 start-page: 724 issue: 3 year: 2009 ident: 5064_CR5 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2008.06.009 – volume: 50 start-page: 297 year: 2010 ident: 5064_CR15 publication-title: Int J Adv Manuf Tech doi: 10.1007/s00170-009-2482-0 – volume: 126 start-page: 2 year: 2004 ident: 5064_CR2 publication-title: J Vib Acoust doi: 10.1115/1.1640638 – volume: 38 start-page: 1156 year: 2003 ident: 5064_CR11 publication-title: Trans Syst Man Cybern – volume: 21 start-page: 2248 year: 2007 ident: 5064_CR3 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2006.10.001 – volume: 61 start-page: 292 issue: 2 year: 2012 ident: 5064_CR12 publication-title: IEEE Trans Reliab doi: 10.1109/TR.2012.2194175 – volume: 23 start-page: 1600 issue: 5 year: 2009 ident: 5064_CR4 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2008.12.006 – volume: 28 start-page: 1012 year: 2006 ident: 5064_CR9 publication-title: Int J Adv Manuf Tech doi: 10.1007/s00170-004-2131-6 – volume-title: Systems dynamics: modeling and simulation of mechatronic systems year: 2006 ident: 5064_CR8 – volume: 17 start-page: 299 year: 2007 ident: 5064_CR13 publication-title: Mechatronics doi: 10.1016/j.mechatronics.2007.03.001 – ident: 5064_CR10 – volume: 28 start-page: 167 year: 2012 ident: 5064_CR18 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2011.10.018 – volume: 20 start-page: 1483 issue: 7 year: 2006 ident: 5064_CR7 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2005.09.012 – volume: 25 start-page: 1803 year: 2011 ident: 5064_CR17 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2010.11.018 – volume: 14 start-page: 71 issue: 1 year: 2006 ident: 5064_CR14 publication-title: Control Eng Pract doi: 10.1016/j.conengprac.2005.01.004 – volume: 29 start-page: 1253 issue: 6 year: 2005 ident: 5064_CR19 publication-title: Comput Chem Eng doi: 10.1016/j.compchemeng.2005.02.026 |
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| SubjectTerms | Automatic CAE) and Design Computer-Aided Engineering (CAD Diagnostic systems Engineering Engineering Sciences Fault detection Fault diagnosis Industrial and Production Engineering Mechanical Engineering Media Management Original Article Useful life |
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| Title | Hybrid prognostic method applied to mechatronic systems |
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