A data-driven health indicator extraction method for aircraft air conditioning system health monitoring
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摘要
Prognostics and Health Management(PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model,one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System(ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System(ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.
Prognostics and Health Management(PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model,one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System(ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System(ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.
Prognostics and Health Management(PHM) has become a very important tool in modern commercial aircraft. Considering limited built-in sensing devices on the legacy aircraft model,one of the challenges for airborne system health monitoring is to find an appropriate health indicator that is highly related to the actual degradation state of the system. This paper proposed a novel health indicator extraction method based on the available sensor parameters for the health monitoring of Air Conditioning System(ACS) of a legacy commercial aircraft model. Firstly, a specific Airplane Condition Monitoring System(ACMS) report for ACS health monitoring is defined. Then a non-parametric modeling technique is adopted to calculate the health indicator based on the raw ACMS report data. The proposed method is validated on a single-aisle commercial aircraft widely used for short and medium-haul routes, using more than 6000 ACMS reports collected from a fleet of aircraft during one year. The case study result shows that the proposed health indicator can effectively characterize the degradation state of the ACS, which can provide valuable information for proactive maintenance plan in advance.
引文
1.Sun JZ,Zuo HF,Wang WB,Pecht MG.Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance.Mech Syst Signal Process2012;28(2):585-96.
2.Smith M,Sulcs P,Walthall R,Mosher M,Kacprzynski G.Design and implementation of aircraft system health management(ASHM)utilizing existing data feeds.Warrendale:SAE International;2015.Report No.:SAE Technical Paper 2015-01-2587.
3.Maggiore JB.Remote management of real-time airplane data Aviation Maint Eng 2008;27(3):22-7.
4.Allen D,Przytula W,Vian J,Mansouri G.Health monitoring for commercial aircraft systems.Congress of the international council of the aeronautics sciences;2008 Sept 14-19;Anchorage,USA.Stockhom:ICAS;2008.p.1-8.
5.Algarni AZ,Tozan M,Algarni A,Jamal A.Failure forecasting of aircraft air conditioning/cooling pack with field data.J Aircraft2012;44(3):996-1002.
6.Hare J,Gupta S,Najjar N,D’Orlando P,Walthall R.System-level fault diagnosis with application to the environmental control system of an aircraft.Warrendale:SAE International;2015.Report No.:SAE Technical Paper 2015-01-2583.
7.Silva A,Najjar N,Gupta S,D’Orlando P,Walthall R.Wavelet-based fouling diagnosis of the heat exchanger in the aircraft environmental control system.Pervasive Mob Comput 2015;9(3):337-8.
8.Najjar N,Hare J,D’Orlando P,Leaper G,Pattipati K,Silva A,et al.Heat exchanger fouling diagnosis for an aircraft airconditioning system.Warrendale:SAE International;2013.Report No.:SAE Technical Paper 2013-01-2250.
9.Najjar N,Gupta S,Hare J,Leaper G,D’Orlando P.Optimal sensor selection and fusion for heat exchanger fouling diagnosis in aerospace systems.Sens J 2016;16(12):4866-81.
10.Shang L,Liu G.Heat exchanger fouling detection in a simulated aircraft engine bleed air temperature control system.2010 IEEE/ASME international conference on advanced intelligent mechatronics;2010 July 6-9;Montreal,Canada.Piscataway(NJ):IEEE Press;2010.p.774-8.
11.Ma J,Lu C,Liu H.Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.Plos One 2015;764-765(3):294-9.
12.Ma MY.Pollution mechanism and cleaning process optimization of heat exchangers in aircraft air-conditioning[dissertation].Tianjin:Civil Aviation University of China;2015[Chinese].
13.Hines JW,Garvey D,Seibert R,Usynin A.Technical review of on-line monitoring techniques for performance assessment,Vol 2:Theoretical issues.Rockville:U.S.Nuclear Regulatory Commission;2007.Report No.:NUREG/CR-6895.
14.Hines JW,Usynin A.MSET performance optimization through regularization.Nucl Eng Technol 2005;37(2):177-84.
15.Singer RM,Gross KC,Herzog JP,King RW,Wegerich S.Modelbased nuclear power plant monitoring and fault detection:Theoretical foundations.The international conference on intelligent systems applications to power systems;1997 July 6-10;Seoul,Korea.Washington,D.C.:USDOE Assistant Secretary for Nuclear Energy;1997.p.60-5.
16.Gribok AV,Urmanov AM,Hines JW.Uncertainty analysis of memory based sensor validation techniques.Real-Time Syst2004;27(1):7-26.
17.Cherkassky V,Mulier F.Learning from data.Hoboken:John Wiley&Sons;1998.
18.Black CL,Uhrig RE,Hines JW.System modeling and instrument calibration verification with a nonlinear state estimation technique.The proceedings of the maintenance and reliability conference;1998 May;Lnoxville,USA.1998.p.12-4.
19.Hines JW,Garvey DR.Traditional and robust vector selection methods for use with similarity based models.5th international topical meeting on nuclear plant instrumentation and control and human-machine interface technologies;2006 Nov 12-16;Albuquerque,USA.La Grange:American Nuclear Society;2006.p.12-6.
20.Fellague K,Nwadiogbu E,Menon S,Borghese J,Patankar R.Heat exchanger fouling detection in aircraft environmental control systems.SAE Tech Papers 2012;5(1):52-6.
21.Rodrigues L.Remaining useful life prediction for multiplecomponent systems based on a system-level performance indicator.IEEE/ASME Transactions on Mechatronics 2017;pp(99):1-10.
2.Smith M,Sulcs P,Walthall R,Mosher M,Kacprzynski G.Design and implementation of aircraft system health management(ASHM)utilizing existing data feeds.Warrendale:SAE International;2015.Report No.:SAE Technical Paper 2015-01-2587.
3.Maggiore JB.Remote management of real-time airplane data Aviation Maint Eng 2008;27(3):22-7.
4.Allen D,Przytula W,Vian J,Mansouri G.Health monitoring for commercial aircraft systems.Congress of the international council of the aeronautics sciences;2008 Sept 14-19;Anchorage,USA.Stockhom:ICAS;2008.p.1-8.
5.Algarni AZ,Tozan M,Algarni A,Jamal A.Failure forecasting of aircraft air conditioning/cooling pack with field data.J Aircraft2012;44(3):996-1002.
6.Hare J,Gupta S,Najjar N,D’Orlando P,Walthall R.System-level fault diagnosis with application to the environmental control system of an aircraft.Warrendale:SAE International;2015.Report No.:SAE Technical Paper 2015-01-2583.
7.Silva A,Najjar N,Gupta S,D’Orlando P,Walthall R.Wavelet-based fouling diagnosis of the heat exchanger in the aircraft environmental control system.Pervasive Mob Comput 2015;9(3):337-8.
8.Najjar N,Hare J,D’Orlando P,Leaper G,Pattipati K,Silva A,et al.Heat exchanger fouling diagnosis for an aircraft airconditioning system.Warrendale:SAE International;2013.Report No.:SAE Technical Paper 2013-01-2250.
9.Najjar N,Gupta S,Hare J,Leaper G,D’Orlando P.Optimal sensor selection and fusion for heat exchanger fouling diagnosis in aerospace systems.Sens J 2016;16(12):4866-81.
10.Shang L,Liu G.Heat exchanger fouling detection in a simulated aircraft engine bleed air temperature control system.2010 IEEE/ASME international conference on advanced intelligent mechatronics;2010 July 6-9;Montreal,Canada.Piscataway(NJ):IEEE Press;2010.p.774-8.
11.Ma J,Lu C,Liu H.Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.Plos One 2015;764-765(3):294-9.
12.Ma MY.Pollution mechanism and cleaning process optimization of heat exchangers in aircraft air-conditioning[dissertation].Tianjin:Civil Aviation University of China;2015[Chinese].
13.Hines JW,Garvey D,Seibert R,Usynin A.Technical review of on-line monitoring techniques for performance assessment,Vol 2:Theoretical issues.Rockville:U.S.Nuclear Regulatory Commission;2007.Report No.:NUREG/CR-6895.
14.Hines JW,Usynin A.MSET performance optimization through regularization.Nucl Eng Technol 2005;37(2):177-84.
15.Singer RM,Gross KC,Herzog JP,King RW,Wegerich S.Modelbased nuclear power plant monitoring and fault detection:Theoretical foundations.The international conference on intelligent systems applications to power systems;1997 July 6-10;Seoul,Korea.Washington,D.C.:USDOE Assistant Secretary for Nuclear Energy;1997.p.60-5.
16.Gribok AV,Urmanov AM,Hines JW.Uncertainty analysis of memory based sensor validation techniques.Real-Time Syst2004;27(1):7-26.
17.Cherkassky V,Mulier F.Learning from data.Hoboken:John Wiley&Sons;1998.
18.Black CL,Uhrig RE,Hines JW.System modeling and instrument calibration verification with a nonlinear state estimation technique.The proceedings of the maintenance and reliability conference;1998 May;Lnoxville,USA.1998.p.12-4.
19.Hines JW,Garvey DR.Traditional and robust vector selection methods for use with similarity based models.5th international topical meeting on nuclear plant instrumentation and control and human-machine interface technologies;2006 Nov 12-16;Albuquerque,USA.La Grange:American Nuclear Society;2006.p.12-6.
20.Fellague K,Nwadiogbu E,Menon S,Borghese J,Patankar R.Heat exchanger fouling detection in aircraft environmental control systems.SAE Tech Papers 2012;5(1):52-6.
21.Rodrigues L.Remaining useful life prediction for multiplecomponent systems based on a system-level performance indicator.IEEE/ASME Transactions on Mechatronics 2017;pp(99):1-10.