民航发动机在翼寿命预测模型方法研究
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摘要
作为飞机的“心脏”,航空发动机的健康状态对保证飞行安全和降低航空公司的运营成本则具有重要意义。本文借助丰富的民航发动机性能监测数据信息,以健康管理理论为基础,全面深入的研究了相关的寿命预测模型方法。
首先,介绍了民机视情维修的维修概念和研究范畴;对民航发动机的状态监控的主要技术进行了介绍,阐述了发动机寿命预测重要性参数的选取准则;最后分别对DEGT和EGTM参数进行分析,确定使用EGTM进行寿命预测的原因,并介绍了影响EGTM的各种因素。确定了发动机在翼寿命预测的参数。为民用发动机在翼寿命预测做好基础工作。
其次,考虑数据质量的问题直接影响发动机寿命预测的准确性,为保证发动机寿命预测的准确性,对发动机性能数据处理方法研究。研究了性能数据采集的内容和采集的途径;分析了数据中存在的质量问题,并提出对采集到的数据的质量的基本要求;接着提出了数据清洗的原理和插值方法,并举例说明清理和差值后的效果。数据的采集和处理为民航发动机寿命预测提供有效的数据支撑。
最后,分析了发动机在翼寿命预测的概念、种类、特点,深入研究了民航发动机剩余寿命预测的方法,包括线性回归方法、分段线性回归方法、基于时间序列以及基于布朗运动的寿命预测方法。应用发动机的运行数据进行了预测方法的线性回归、分段线性回归、基于时间序列以及基于布朗运动实例验证。
By way of airplane heart, the health of aero engines has important signification for the safe of airplane and the decrease of operation cost. This paper based on the rich civil aviation engine performance monitoring data information to health management theory as a foundation, further comprehensive study the relevant life prediction model method.
First of all, introduced the Condition Based Maintenance concept and research category, introduced the main technology of condition monitoring for civil aviation engine. Finally, analysis the parameters of DEGT and EGTM, and make sure the reasons to use EGTM as the parameters of life prediction.
Next, consider the data quality problems directly affecting the accuracy of the engine life prediction, and in order to ensure the accuracy of the engine life prediction, engine performance data processing method is studied in this paper. At the same time, the paper studied the content and means of performance data collection and data quality problems, and proposed the basic requirements of quality in data collection; then proposed the data cleaning theory and interpolation methods, and examples was given to show the effect of difference after the data cleaning and interpolation . Data collection and processing provide effective data support to the engine life prediction.
Finally, the paper analyzed the concept, types, characteristics of engine life prediction, and studied of the civil aviation engine remaining life prediction methods, including linear regression, piecewise linear regression method, based on time series and life prediction based on Brownian motion methods. Numerical examples are given which illustrate the effectiveness of the life prediction approach.
首先,介绍了民机视情维修的维修概念和研究范畴;对民航发动机的状态监控的主要技术进行了介绍,阐述了发动机寿命预测重要性参数的选取准则;最后分别对DEGT和EGTM参数进行分析,确定使用EGTM进行寿命预测的原因,并介绍了影响EGTM的各种因素。确定了发动机在翼寿命预测的参数。为民用发动机在翼寿命预测做好基础工作。
其次,考虑数据质量的问题直接影响发动机寿命预测的准确性,为保证发动机寿命预测的准确性,对发动机性能数据处理方法研究。研究了性能数据采集的内容和采集的途径;分析了数据中存在的质量问题,并提出对采集到的数据的质量的基本要求;接着提出了数据清洗的原理和插值方法,并举例说明清理和差值后的效果。数据的采集和处理为民航发动机寿命预测提供有效的数据支撑。
最后,分析了发动机在翼寿命预测的概念、种类、特点,深入研究了民航发动机剩余寿命预测的方法,包括线性回归方法、分段线性回归方法、基于时间序列以及基于布朗运动的寿命预测方法。应用发动机的运行数据进行了预测方法的线性回归、分段线性回归、基于时间序列以及基于布朗运动实例验证。
By way of airplane heart, the health of aero engines has important signification for the safe of airplane and the decrease of operation cost. This paper based on the rich civil aviation engine performance monitoring data information to health management theory as a foundation, further comprehensive study the relevant life prediction model method.
First of all, introduced the Condition Based Maintenance concept and research category, introduced the main technology of condition monitoring for civil aviation engine. Finally, analysis the parameters of DEGT and EGTM, and make sure the reasons to use EGTM as the parameters of life prediction.
Next, consider the data quality problems directly affecting the accuracy of the engine life prediction, and in order to ensure the accuracy of the engine life prediction, engine performance data processing method is studied in this paper. At the same time, the paper studied the content and means of performance data collection and data quality problems, and proposed the basic requirements of quality in data collection; then proposed the data cleaning theory and interpolation methods, and examples was given to show the effect of difference after the data cleaning and interpolation . Data collection and processing provide effective data support to the engine life prediction.
Finally, the paper analyzed the concept, types, characteristics of engine life prediction, and studied of the civil aviation engine remaining life prediction methods, including linear regression, piecewise linear regression method, based on time series and life prediction based on Brownian motion methods. Numerical examples are given which illustrate the effectiveness of the life prediction approach.
引文
[1]左洪福.发动机磨损状态监测与故障诊断技术.北京:航空工业出版社,1996,10.
[2] Li Y G. Performance-analysis-based gas turbine diagnostics: a review. Proceedings of the Institution of Mechanical Engineers - Part A: Journal Power and Energy, 2002, 216(5):363-377.
[3] USAF. Condition-based maintenance plus(CBM+) initiative[R].Air force Logististics Management Agnecy,2003
[4]白芳.民航发动机机群预知维修决策理论与方法研究[D]. [博士学位论文],南京:南京航空航天大学, 2009.
[5] Bickford R L. Health management and controls for Earth-to-orbit propulsion systems [J]. Acta Astronautica, 1995, 35(6):411-419.
[6] J. J. Gill, B. F. G. Aerosp, M. A. Bedford (2000), Lessons Learned from Rotary-and Fixed-Wing HUMS Applications, Aerospace Conference Proceedings, IEEE.
[7]曾声奎, Michael G.Pecht,吴际(2005).故障预测与健康管理(PHM)技术的现状与发展,航空学报. Vol.26 No.5 P.626-632.
[8]潘全文,李天,李行善.预测与健康管理系统体系结构研究.第十七届全国测控计量仪器仪表学术年会(MCMI'2007)论文集(上册),中国福建厦门, 2007: 32-37.
[9]木志高,胡海峰,胡茑庆.武器装备故障预测及健康管理系统设计.兵工自动化,2006, 25(3):20-21.
[10]梁工谦,陈翔宇.视情维修中的设备维修模式权衡分析.西北工业大学学报, 2005, 23(4):504-507.
[11]孙博,康锐,谢劲松.PHM系统中的传感器应用与数据传输技术.测控技术,2007, 26(7):12-14.
[12] LiC, LeiY. Fault Diagnosis for an Aircraft Engine Based on Information Fusion. Mechatronics, 2006 IEEE International Conference on, IEEE, 2006:199-202.
[13]黄伟斌.发动机健康管理的自适应机载实时模型.[硕士],南京,南京航空航天大学, 2007.
[14]王萍.先进传感器技术在飞机故障诊断中的应用.测控技术,2007,26(3):27-29.
[15]张叔农,谢劲松,康锐.电子产品健康监控和故障预测技术框架.测控技术,2007, 26(2):12-16,18.
[16]徐萍,康锐.预测与状态管理系统(PHM)技术研究.测控技术,2004,23(12):58-60.
[17]孙博,康锐,谢劲松.故障预测与健康管理系统研究和应用现状综述.系统工程与电子技术,2007,29(10):1762-1767.
[18]左洪福.发动机磨损状态监测与故障诊断技术.北京:航空工业出版社,1996,10.
[19] S. Vittal, P. Hajela, A. Joshi (2004), Review of Approaches to gas turbine life management, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, New York.
[20]张秀斌,王广伟,郭波.应用比例故障率模型进行基于状态的视情维修决策.电子产品可靠性与环境试验, 2002(4):19-22.
[21]王政,朱晓军,杨铁军, et al.视情维修检测间隔模糊预测研究.中国修船, 2006, 19(6):46-48.
[22]左洪福,张海军,戎翔.基于比例风险模型的航空发动机视情维修决策.航空动力学报, 2006, 21(4):716-721.
[23]白芳,左洪福,文振华, et al.基于视情维修策略的航空发动机调度方法.交通运输工程学报, 2007, 7(3):29-33.
[24]梁旭,李行善,张磊, et al.支持视情维修的故障预测技术研究.测控技术, 2007, 26(6):5-9.
[25] Marcus Bengtsson, Condition Based Maintenance System Technology[J] - Where is Development Heading, Euro maintenance 2004– Proceedings of 17th European Maintenance Congress.
[26]宋金光,许春生.基于遗传算法和BP网络的航空发动机拆换期望值预测[J].航空动力学报, 2003,(05) .
[27] Suarez, E. L. Hansen, J. P. Duffy, M. J. Gatlin, P. K. (1997), New approach to tracking engine life, AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 33rd.
[28]贺尔铭,宋力涛. EGT影响因素分析及提高EGT裕度的措施[J].航空维修,1996,(6) :20-21.
[29] Marcus Bengtsson, Condition Based Maintenance System Technology[J] - Where is Development Heading, Euro maintenance 2004– Proceedings of 17th European Maintenance Congress.
[30] (英)J.莫布雷,以可靠性为中心的维修[M],北京,机械工业出版社,1995
[31] Tsang a H C, Yeung W K, Jardine a K S, et al. Data management for CBM optimization. Journal of Quality Maintenance Engineering, 2006, 12(1):37-51.
[32]朱睿,刘槟.飞机健康管理数据挖掘方法研究[J].中国民航学院学报, 2004, 22(S1):150-153.
[33] Gary R. Weckman, Robert A. McLauchlan. Using neural networks to forecast jet engine removals for restoration. The ASME Artificial Neural Networks in engineering Conference (ANNIE'99), St.Louis, Missouri, USA, November 7-10,1999, 1189~1194
[34]郝英.基于智能技术的民航发动机故障诊断和寿命预测研究,博士学位论文,南京航空航天大学,2006
[35]贺尔铭,宋力涛.EGT影响因素分析及提高EGT裕度的措施.航空维修,1996,(6) :20-21
[36] Redman T, The Impact of Poor Data Quality on the Typical Enterprise, CACM, 1998, 41(2): 79-82
[37]陆静,赵阳,数据预处理中的数据集成和变换研究,福建电脑,2008年,第1期:108～111.
[38]潘红宇.时间序列分析.北京:对外经济贸易大学出版社,2006.
[39]陈伟,数据清理关键技术及其软件平台的研究与应用,博士学位论文,南京,南京航空航天大学, 2004.6
[40]张海军.民航发动机性能综合评估与视情维修决策建模方法研究[D].博士学位论文,南京:南京航空航天大学, 2007.
[41] Zuo M.J., Jiang R., and Yam R. C. M.. Approaches for reliability modeling of continuous-state devices [J]. IEEE Trans. Reliab., 1999, 48(1): 9-18.
[42]王烨.民机产品可靠性技术研究[D]. [博士学位论文],南京:南京航空航天大学, 2009.
[43] Siegmund D. Boundary crossing probabilities and statistical applications [J]. Ann. Statist. 1986, 14: 361-404.
[44]张波.应用随机过程[M].北京:中国人民大学出版社, 2001.
[45]周源泉,刘文生.双参数指数分布的可靠性评估(Ⅰ) [J].质量与可靠性, 2004(1):5-10.
[46]徐可君,江龙平.军用航空发动机可靠性和寿命管理[J].中国工程科学, 2003, 5(1):82-88.
[47]徐可君.基于单元体的军用航空发动机寿命控制和管理[J].海军航空工程学院学报, 2007, 22(5):541-547.
[48] Huang W.. Reliability Analysis Considering Product Performance Degradation [D]. PhDThesis, The University of Arizona, 2002.
[2] Li Y G. Performance-analysis-based gas turbine diagnostics: a review. Proceedings of the Institution of Mechanical Engineers - Part A: Journal Power and Energy, 2002, 216(5):363-377.
[3] USAF. Condition-based maintenance plus(CBM+) initiative[R].Air force Logististics Management Agnecy,2003
[4]白芳.民航发动机机群预知维修决策理论与方法研究[D]. [博士学位论文],南京:南京航空航天大学, 2009.
[5] Bickford R L. Health management and controls for Earth-to-orbit propulsion systems [J]. Acta Astronautica, 1995, 35(6):411-419.
[6] J. J. Gill, B. F. G. Aerosp, M. A. Bedford (2000), Lessons Learned from Rotary-and Fixed-Wing HUMS Applications, Aerospace Conference Proceedings, IEEE.
[7]曾声奎, Michael G.Pecht,吴际(2005).故障预测与健康管理(PHM)技术的现状与发展,航空学报. Vol.26 No.5 P.626-632.
[8]潘全文,李天,李行善.预测与健康管理系统体系结构研究.第十七届全国测控计量仪器仪表学术年会(MCMI'2007)论文集(上册),中国福建厦门, 2007: 32-37.
[9]木志高,胡海峰,胡茑庆.武器装备故障预测及健康管理系统设计.兵工自动化,2006, 25(3):20-21.
[10]梁工谦,陈翔宇.视情维修中的设备维修模式权衡分析.西北工业大学学报, 2005, 23(4):504-507.
[11]孙博,康锐,谢劲松.PHM系统中的传感器应用与数据传输技术.测控技术,2007, 26(7):12-14.
[12] LiC, LeiY. Fault Diagnosis for an Aircraft Engine Based on Information Fusion. Mechatronics, 2006 IEEE International Conference on, IEEE, 2006:199-202.
[13]黄伟斌.发动机健康管理的自适应机载实时模型.[硕士],南京,南京航空航天大学, 2007.
[14]王萍.先进传感器技术在飞机故障诊断中的应用.测控技术,2007,26(3):27-29.
[15]张叔农,谢劲松,康锐.电子产品健康监控和故障预测技术框架.测控技术,2007, 26(2):12-16,18.
[16]徐萍,康锐.预测与状态管理系统(PHM)技术研究.测控技术,2004,23(12):58-60.
[17]孙博,康锐,谢劲松.故障预测与健康管理系统研究和应用现状综述.系统工程与电子技术,2007,29(10):1762-1767.
[18]左洪福.发动机磨损状态监测与故障诊断技术.北京:航空工业出版社,1996,10.
[19] S. Vittal, P. Hajela, A. Joshi (2004), Review of Approaches to gas turbine life management, 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, New York.
[20]张秀斌,王广伟,郭波.应用比例故障率模型进行基于状态的视情维修决策.电子产品可靠性与环境试验, 2002(4):19-22.
[21]王政,朱晓军,杨铁军, et al.视情维修检测间隔模糊预测研究.中国修船, 2006, 19(6):46-48.
[22]左洪福,张海军,戎翔.基于比例风险模型的航空发动机视情维修决策.航空动力学报, 2006, 21(4):716-721.
[23]白芳,左洪福,文振华, et al.基于视情维修策略的航空发动机调度方法.交通运输工程学报, 2007, 7(3):29-33.
[24]梁旭,李行善,张磊, et al.支持视情维修的故障预测技术研究.测控技术, 2007, 26(6):5-9.
[25] Marcus Bengtsson, Condition Based Maintenance System Technology[J] - Where is Development Heading, Euro maintenance 2004– Proceedings of 17th European Maintenance Congress.
[26]宋金光,许春生.基于遗传算法和BP网络的航空发动机拆换期望值预测[J].航空动力学报, 2003,(05) .
[27] Suarez, E. L. Hansen, J. P. Duffy, M. J. Gatlin, P. K. (1997), New approach to tracking engine life, AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, 33rd.
[28]贺尔铭,宋力涛. EGT影响因素分析及提高EGT裕度的措施[J].航空维修,1996,(6) :20-21.
[29] Marcus Bengtsson, Condition Based Maintenance System Technology[J] - Where is Development Heading, Euro maintenance 2004– Proceedings of 17th European Maintenance Congress.
[30] (英)J.莫布雷,以可靠性为中心的维修[M],北京,机械工业出版社,1995
[31] Tsang a H C, Yeung W K, Jardine a K S, et al. Data management for CBM optimization. Journal of Quality Maintenance Engineering, 2006, 12(1):37-51.
[32]朱睿,刘槟.飞机健康管理数据挖掘方法研究[J].中国民航学院学报, 2004, 22(S1):150-153.
[33] Gary R. Weckman, Robert A. McLauchlan. Using neural networks to forecast jet engine removals for restoration. The ASME Artificial Neural Networks in engineering Conference (ANNIE'99), St.Louis, Missouri, USA, November 7-10,1999, 1189~1194
[34]郝英.基于智能技术的民航发动机故障诊断和寿命预测研究,博士学位论文,南京航空航天大学,2006
[35]贺尔铭,宋力涛.EGT影响因素分析及提高EGT裕度的措施.航空维修,1996,(6) :20-21
[36] Redman T, The Impact of Poor Data Quality on the Typical Enterprise, CACM, 1998, 41(2): 79-82
[37]陆静,赵阳,数据预处理中的数据集成和变换研究,福建电脑,2008年,第1期:108～111.
[38]潘红宇.时间序列分析.北京:对外经济贸易大学出版社,2006.
[39]陈伟,数据清理关键技术及其软件平台的研究与应用,博士学位论文,南京,南京航空航天大学, 2004.6
[40]张海军.民航发动机性能综合评估与视情维修决策建模方法研究[D].博士学位论文,南京:南京航空航天大学, 2007.
[41] Zuo M.J., Jiang R., and Yam R. C. M.. Approaches for reliability modeling of continuous-state devices [J]. IEEE Trans. Reliab., 1999, 48(1): 9-18.
[42]王烨.民机产品可靠性技术研究[D]. [博士学位论文],南京:南京航空航天大学, 2009.
[43] Siegmund D. Boundary crossing probabilities and statistical applications [J]. Ann. Statist. 1986, 14: 361-404.
[44]张波.应用随机过程[M].北京:中国人民大学出版社, 2001.
[45]周源泉,刘文生.双参数指数分布的可靠性评估(Ⅰ) [J].质量与可靠性, 2004(1):5-10.
[46]徐可君,江龙平.军用航空发动机可靠性和寿命管理[J].中国工程科学, 2003, 5(1):82-88.
[47]徐可君.基于单元体的军用航空发动机寿命控制和管理[J].海军航空工程学院学报, 2007, 22(5):541-547.
[48] Huang W.. Reliability Analysis Considering Product Performance Degradation [D]. PhDThesis, The University of Arizona, 2002.