航天发射塔回转平台状态质量检测技术研究
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摘要
航天发射场地面设施设备是执行科研试验任务的重要物质基础,其质量的好坏直接影响科研试验任务的成败。因此开展对航天发射塔回转平台状态质量检测技术的研究具有重要的现实意义和理论意义。
往复式低速重载旋转不完全大轴承是实现回转平台功能的一个关键部件,此类轴承由于其特殊的运行特点,传统轴承的故障诊断方法无法直接利用。对这种轴承的性能检测和故障诊断研究,是一个全新的领域。因此,本学位论文研究了滚动轴承的故障起因及故障类型,分析了各种故障的振动特点,总结了往复式低速重载旋转不完全大轴承的振动检测原理,为状态质量检测提供了理论依据,并据此设计了检测系统。在试验基础上,分析了回转平台在打开与合拢时的振动信号,首次提出了从脉冲能量法角度对往复式低速重载旋转不完全大轴承的状态质量进行检测,构造了加权平均的脉冲总能量和脉冲冲击次数特征参量,由此推导了回转平台的状态质量检测方法,并建立了基于BP神经网络的状态质量模式识别系统。
通过本文的研究,实现了航天发射塔回转平台状态质量的有效检测,为航天发射任务的圆满完成提供了前提和保障,有一定的应用前景和较高的实用价值。
The spaceflight launch site equipment is the matter foundation for experiment, which is directly related to the success or failure of it. This paper contributes effective theory and methods for the spaceflight launch site equipment.
The key part of the rotary flat is the rolling bearing of reciprocating,low speed, non-full rotation and heavy load. Because of the rolling bearing’s special work characteristic, traditional fault diagnosis don’t directly use. It is fully new study area for the fault diagnosis of the rolling bearing. The paper firstly study the cause and type of fault in rolling bearing, analyze the vibration feature of all kinds of faults and summarize the vibration detection theory of this type bearing. They are the principle basis of state quality test. Test system is designed. Based on the experiment, it firstly put forward pulse energy for the state quality test for the rolling bearing of reciprocating,low speed, non-full rotation and heavy load by analyzing on-off vibration signals. The parameter of pulse energy and pulse times are constructed, which is used to solve the state quality test for rotary flat .Set up the state quality pattern recognition system based on BP neural network.
The performance test and evaluation on state quality of rotary flat are primarily realized. The method has certain application foreground and high practicality value.
往复式低速重载旋转不完全大轴承是实现回转平台功能的一个关键部件,此类轴承由于其特殊的运行特点,传统轴承的故障诊断方法无法直接利用。对这种轴承的性能检测和故障诊断研究,是一个全新的领域。因此,本学位论文研究了滚动轴承的故障起因及故障类型,分析了各种故障的振动特点,总结了往复式低速重载旋转不完全大轴承的振动检测原理,为状态质量检测提供了理论依据,并据此设计了检测系统。在试验基础上,分析了回转平台在打开与合拢时的振动信号,首次提出了从脉冲能量法角度对往复式低速重载旋转不完全大轴承的状态质量进行检测,构造了加权平均的脉冲总能量和脉冲冲击次数特征参量,由此推导了回转平台的状态质量检测方法,并建立了基于BP神经网络的状态质量模式识别系统。
通过本文的研究,实现了航天发射塔回转平台状态质量的有效检测,为航天发射任务的圆满完成提供了前提和保障,有一定的应用前景和较高的实用价值。
The spaceflight launch site equipment is the matter foundation for experiment, which is directly related to the success or failure of it. This paper contributes effective theory and methods for the spaceflight launch site equipment.
The key part of the rotary flat is the rolling bearing of reciprocating,low speed, non-full rotation and heavy load. Because of the rolling bearing’s special work characteristic, traditional fault diagnosis don’t directly use. It is fully new study area for the fault diagnosis of the rolling bearing. The paper firstly study the cause and type of fault in rolling bearing, analyze the vibration feature of all kinds of faults and summarize the vibration detection theory of this type bearing. They are the principle basis of state quality test. Test system is designed. Based on the experiment, it firstly put forward pulse energy for the state quality test for the rolling bearing of reciprocating,low speed, non-full rotation and heavy load by analyzing on-off vibration signals. The parameter of pulse energy and pulse times are constructed, which is used to solve the state quality test for rotary flat .Set up the state quality pattern recognition system based on BP neural network.
The performance test and evaluation on state quality of rotary flat are primarily realized. The method has certain application foreground and high practicality value.
引文
[1] 陈进,机械设备振动监测与故障诊断,上海:上海交通大学出版社,1999
[2] 刘雄,赵振毅,转子监测和诊断系统,西安:西安交通大学出版社,1991
[3] 韩捷,张瑞林等,旋转机械故障机理及诊断技术,北京:机械工业出版社,1997
[4] R.H.Lyon, R.G.Dejonj. Design of s high-level diagnostic system. Journal of vibration, Acousties, Stress and Reliability in Design, 1984 (106):17-21
[5] P.D.McFadden and M.M.Toozhy. Application of synchronous averaging to vibration monitoring of rolling element bearing. Mechanical Systems and Signal Processing, 2000, 14(6):891-906.
[6] N. Tandon and A. Choudury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International,1999, 32:469-480
[7] T.Williams, X.Ribadeneira and S. Billingto. Rolling element bearing diagnostics in run-to-failure lifetime testing. Mechanical Systems and Signal Processing, 2001,l 5(5): 979-993.
[8] J.P Dron, L. Rasolofondraibe and F. Bolaers. High-resolution method in vibratory analysis: application to ball bearing monitoring and production machine. International Journal of Solids and Structures, 2001, 38:4293-4313.
[9] Robinson ,J .C .,Canada ,R.G. and Piety ,R .G. Vibration Monitoring on Slow Speed Machinery :New Methodologies Covering Machinery from 0.5 to 600rpm.The Fifth International Conference on Profitable Condition Monitoring一Fluids and Machinery Performance Monitoring :BHR Group Publication, 1996
[10] Kuboyama , K .Development of Low Speed Bearing Diagnosis Technique, Fukuyama City, Japan, 1997. Hiroshima:NKK Fukuyama, 1997
[11] 赵飞鹏,吴晚云,郝保国。低速重载轴承的故障诊断。冶金设备,1999,6(3):40-42.
[12] 王志刚,李友荣,朱瑞荪,吕勇。一种用于低速重载轴承故障诊断的共振解调法。 煤矿机械,2002,8:74-76.
[13] Jaehong Suh. On-line machinery health diagnosis and prognosis for predictive maintenance and quality assurance of equipment functioning. Dissertation for the Degree of Doctor of Philosophy, the Pennsylvania State University, 2001.
[14] Peng Wang. Intelligent signal/image processing for fault diagnosis and prognosis. Dissertation for the Degree of Doctor of Philosophy,Georgia Institute of Technology, 2000.
[15] C. James Li and Jun Ma. Wavelet decomposition of vibrations for detection of bearing-localized defects. NDT&E International, 1997, 30(3):143-149.
[16] 程军圣,于德介。基于小波系数 1-1-2 维谱的滚动轴承故障诊断。振动与冲击,2001,20(3):29-31.
[17] 何晓霞,沈玉娣,张西宁。连续小波变换在滚动轴承故障诊断中的应用。机械科学与技术,2001, 20(4):571-574 .
[18] 沈松,应怀樵,刘进明。用小波变换识别机械故障中的通过振动。振动与冲击, 1999 ,18(2):1-5.
[19] 沈松,应怀樵,刘进明。小波变换在振动信号分析中的工程解释与应用。振动、测试与诊断,2000, 20(4):260-263.
[20] 奚定平,李雄军。基于小波变换在线检测机器运行时轴承产生的缺陷。振动、测试与诊断,2000, 20(4):274-278.
[21] 史东锋,鲍明,屈梁生。小波包络分析在滚动轴承诊断中的应用。中国机械工程,2000, 11(12):1382-1385.
[22] 刘玉斌,师杭军,郭伟等。小波分析在滚动轴承状态监测振动信号分析中的应用。煤矿机械,2001, 3:63-65 .
[23] 何岭松。小波函数性质及其对小波分析结果的影响。振动工程学报,2000, 13(1): 143-146.
[24] 林京。基于连续小波变换的信号检测技术与故障诊断。机械工程学报, 2000,36(12):95-100.
[25] 林京。连续小波变换及其在滚动轴承故障诊断中的应用。西安交通大学学报,1999,33(11):108-110.
[26] 王新晴,王耀华,熊云。基于小波除噪与时一频指数分布的故障诊断新技术。解放军理工大学学报,2001, Vol2(2):59-61.
[27] 程军圣,于德介。离散余弦变换在滚动轴承故障诊断中的应用。振动与冲击,2000,19(3):20-22.
[28] 陆爽,曲守平,张小辉。小波分析在轴承故障诊断中的应用。长春大学学报,2000,10(6):5-7.
[29] 彭桂兰,薛新英。小波分析在机械故障诊断中的应用。塔里木农垦大学学报,2000,12(1).
[30] S. Prabhakar, A.R. Monhanty and A.S. Sekhar. Application of discrete wavelet transform for detection of ball bearing race faults. Tribology International, 2002, 35:793-800.
[31] K. Mori, N. Kasashima and T. Yoshioka. Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals. Wear, 1996, 195:162-168.
[32] J.Altmann and J.Mathew.Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis. Mechanical Systems and Signal Processing, 2001,15(5):963-977.
[33] R. Rubini, U. Meneghetti. Application of the envelope and wavelet transform analysis for the diagnosis of Incipient faults In ball bearings. Mechanical Systems and Signal Processing, 2001,15(2):287-302.
[34] 黄德双,神经网络模式识别系统理论[M]. 北京:电子工业出版社,1996: 45-47.
[35] Fang Tu, Fang Wen, Peter Willett, Krishna Pattipati, Eric H.Jordan. Signal processing and neural network toolbox and its application to failure diagnosis and prognosis. SPIE-PROC.2001.4389:121-133.
[36]傅瑜。小波分析在旋转机械故障诊断中的应用。西北电子科技大学博士学位论文,1998.
[37] 王汉功,徐远国,李志明. 装备全面质量管理. 国防工业出版社,2003:100～118.
[38] 吴伟仁,高志强,杨为民.国防科技工业领导干部质量与可靠性培训教材,国防科学技术工业委员会科技与质量司,2003:249～250.
[39] 徐章遂,房立清. 故障信息诊断原理及应用. 国防工业出版社,2000:17～23.
[40] 马飞. 基于虚拟仪器的发动机噪声测试系统. 武汉理工大学硕士学位论文,2003.
[41] 赵松年,熊小芸. 子波变换与子波分析. 北京:电子工业出版社,1996.
[42] 程正兴. 小波分析算法与应用. 西安:西安交通大学出版社. 1998.
[43] 胡昌华等. 基于 MATLAB 的系统分析与设计─小波分析. 西安电子科技大学出版社. 1999
[44] 徐长发,李国宽. 实用小波方法. 华中科技大学出版社. 2001
[45] 钟秉林、黄仁.机械故障诊断学.机械工业出版社.2002
[46] 袁曾任. 人工神经元网络及其应用. 北京:清华大学出版社,1999,10
[47] Li, Y., Pont M.J., Parikh, C.R. & Jones, N.B. A comparison of the performance of Radial Basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications. Condition Monitoring'99, Jones, M.H. and Sleeman, D.G. (eds.), pp577-592. Coxmoor Publishing, ISBN: 901892115.
[48] Li, Y., Jones, N.B. & Pont M.J. Applying neural networks and fuzzy logic to fault diagnosis: A review. In: John, R.and Birkenhead, R. (Eds) Proceedings of:'Workshop on Recent Advances in Soft Computing', De Montfort University, Leicester, July 1998, pp.104-119 ISBN 185 721 2592
[49] Parikh, C.R., Pont, M.J., Li, Y.H., and Jones, N.B., (1999), Neural Networks for Condition Monitoring and Fault Diagnosis: The Effect of Training Data on Classifier Performance, in Condition Monitoring'99, Jones, M.H. and Sleeman, D.G. (eds.), Coxmoor Publishing, ISBN: 901892115.
[50] Kosko, B., Neural networks and fuzzy systems, Prentice Hall, Inc. 1992
[51] Parikh, C.R., Pont, M.J., and Jones, N.B., (1999), Improving the Performance of Multi-layer Perceptrons where Limited Training Data are Available for Some Classes, 9th International Conference on Artificial Neural Networks (ICANN99), Edinburgh, IEE Conference publication No. 470, pp. 227-232.
[52] 焦李成. 神经网络系统理论. 西安电子科技大学出版社,1990.12-32.
[2] 刘雄,赵振毅,转子监测和诊断系统,西安:西安交通大学出版社,1991
[3] 韩捷,张瑞林等,旋转机械故障机理及诊断技术,北京:机械工业出版社,1997
[4] R.H.Lyon, R.G.Dejonj. Design of s high-level diagnostic system. Journal of vibration, Acousties, Stress and Reliability in Design, 1984 (106):17-21
[5] P.D.McFadden and M.M.Toozhy. Application of synchronous averaging to vibration monitoring of rolling element bearing. Mechanical Systems and Signal Processing, 2000, 14(6):891-906.
[6] N. Tandon and A. Choudury. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International,1999, 32:469-480
[7] T.Williams, X.Ribadeneira and S. Billingto. Rolling element bearing diagnostics in run-to-failure lifetime testing. Mechanical Systems and Signal Processing, 2001,l 5(5): 979-993.
[8] J.P Dron, L. Rasolofondraibe and F. Bolaers. High-resolution method in vibratory analysis: application to ball bearing monitoring and production machine. International Journal of Solids and Structures, 2001, 38:4293-4313.
[9] Robinson ,J .C .,Canada ,R.G. and Piety ,R .G. Vibration Monitoring on Slow Speed Machinery :New Methodologies Covering Machinery from 0.5 to 600rpm.The Fifth International Conference on Profitable Condition Monitoring一Fluids and Machinery Performance Monitoring :BHR Group Publication, 1996
[10] Kuboyama , K .Development of Low Speed Bearing Diagnosis Technique, Fukuyama City, Japan, 1997. Hiroshima:NKK Fukuyama, 1997
[11] 赵飞鹏,吴晚云,郝保国。低速重载轴承的故障诊断。冶金设备,1999,6(3):40-42.
[12] 王志刚,李友荣,朱瑞荪,吕勇。一种用于低速重载轴承故障诊断的共振解调法。 煤矿机械,2002,8:74-76.
[13] Jaehong Suh. On-line machinery health diagnosis and prognosis for predictive maintenance and quality assurance of equipment functioning. Dissertation for the Degree of Doctor of Philosophy, the Pennsylvania State University, 2001.
[14] Peng Wang. Intelligent signal/image processing for fault diagnosis and prognosis. Dissertation for the Degree of Doctor of Philosophy,Georgia Institute of Technology, 2000.
[15] C. James Li and Jun Ma. Wavelet decomposition of vibrations for detection of bearing-localized defects. NDT&E International, 1997, 30(3):143-149.
[16] 程军圣,于德介。基于小波系数 1-1-2 维谱的滚动轴承故障诊断。振动与冲击,2001,20(3):29-31.
[17] 何晓霞,沈玉娣,张西宁。连续小波变换在滚动轴承故障诊断中的应用。机械科学与技术,2001, 20(4):571-574 .
[18] 沈松,应怀樵,刘进明。用小波变换识别机械故障中的通过振动。振动与冲击, 1999 ,18(2):1-5.
[19] 沈松,应怀樵,刘进明。小波变换在振动信号分析中的工程解释与应用。振动、测试与诊断,2000, 20(4):260-263.
[20] 奚定平,李雄军。基于小波变换在线检测机器运行时轴承产生的缺陷。振动、测试与诊断,2000, 20(4):274-278.
[21] 史东锋,鲍明,屈梁生。小波包络分析在滚动轴承诊断中的应用。中国机械工程,2000, 11(12):1382-1385.
[22] 刘玉斌,师杭军,郭伟等。小波分析在滚动轴承状态监测振动信号分析中的应用。煤矿机械,2001, 3:63-65 .
[23] 何岭松。小波函数性质及其对小波分析结果的影响。振动工程学报,2000, 13(1): 143-146.
[24] 林京。基于连续小波变换的信号检测技术与故障诊断。机械工程学报, 2000,36(12):95-100.
[25] 林京。连续小波变换及其在滚动轴承故障诊断中的应用。西安交通大学学报,1999,33(11):108-110.
[26] 王新晴,王耀华,熊云。基于小波除噪与时一频指数分布的故障诊断新技术。解放军理工大学学报,2001, Vol2(2):59-61.
[27] 程军圣,于德介。离散余弦变换在滚动轴承故障诊断中的应用。振动与冲击,2000,19(3):20-22.
[28] 陆爽,曲守平,张小辉。小波分析在轴承故障诊断中的应用。长春大学学报,2000,10(6):5-7.
[29] 彭桂兰,薛新英。小波分析在机械故障诊断中的应用。塔里木农垦大学学报,2000,12(1).
[30] S. Prabhakar, A.R. Monhanty and A.S. Sekhar. Application of discrete wavelet transform for detection of ball bearing race faults. Tribology International, 2002, 35:793-800.
[31] K. Mori, N. Kasashima and T. Yoshioka. Prediction of spalling on a ball bearing by applying the discrete wavelet transform to vibration signals. Wear, 1996, 195:162-168.
[32] J.Altmann and J.Mathew.Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis. Mechanical Systems and Signal Processing, 2001,15(5):963-977.
[33] R. Rubini, U. Meneghetti. Application of the envelope and wavelet transform analysis for the diagnosis of Incipient faults In ball bearings. Mechanical Systems and Signal Processing, 2001,15(2):287-302.
[34] 黄德双,神经网络模式识别系统理论[M]. 北京:电子工业出版社,1996: 45-47.
[35] Fang Tu, Fang Wen, Peter Willett, Krishna Pattipati, Eric H.Jordan. Signal processing and neural network toolbox and its application to failure diagnosis and prognosis. SPIE-PROC.2001.4389:121-133.
[36]傅瑜。小波分析在旋转机械故障诊断中的应用。西北电子科技大学博士学位论文,1998.
[37] 王汉功,徐远国,李志明. 装备全面质量管理. 国防工业出版社,2003:100～118.
[38] 吴伟仁,高志强,杨为民.国防科技工业领导干部质量与可靠性培训教材,国防科学技术工业委员会科技与质量司,2003:249～250.
[39] 徐章遂,房立清. 故障信息诊断原理及应用. 国防工业出版社,2000:17～23.
[40] 马飞. 基于虚拟仪器的发动机噪声测试系统. 武汉理工大学硕士学位论文,2003.
[41] 赵松年,熊小芸. 子波变换与子波分析. 北京:电子工业出版社,1996.
[42] 程正兴. 小波分析算法与应用. 西安:西安交通大学出版社. 1998.
[43] 胡昌华等. 基于 MATLAB 的系统分析与设计─小波分析. 西安电子科技大学出版社. 1999
[44] 徐长发,李国宽. 实用小波方法. 华中科技大学出版社. 2001
[45] 钟秉林、黄仁.机械故障诊断学.机械工业出版社.2002
[46] 袁曾任. 人工神经元网络及其应用. 北京:清华大学出版社,1999,10
[47] Li, Y., Pont M.J., Parikh, C.R. & Jones, N.B. A comparison of the performance of Radial Basis function and multi-layer perceptron networks in condition monitoring and fault diagnosis applications. Condition Monitoring'99, Jones, M.H. and Sleeman, D.G. (eds.), pp577-592. Coxmoor Publishing, ISBN: 901892115.
[48] Li, Y., Jones, N.B. & Pont M.J. Applying neural networks and fuzzy logic to fault diagnosis: A review. In: John, R.and Birkenhead, R. (Eds) Proceedings of:'Workshop on Recent Advances in Soft Computing', De Montfort University, Leicester, July 1998, pp.104-119 ISBN 185 721 2592
[49] Parikh, C.R., Pont, M.J., Li, Y.H., and Jones, N.B., (1999), Neural Networks for Condition Monitoring and Fault Diagnosis: The Effect of Training Data on Classifier Performance, in Condition Monitoring'99, Jones, M.H. and Sleeman, D.G. (eds.), Coxmoor Publishing, ISBN: 901892115.
[50] Kosko, B., Neural networks and fuzzy systems, Prentice Hall, Inc. 1992
[51] Parikh, C.R., Pont, M.J., and Jones, N.B., (1999), Improving the Performance of Multi-layer Perceptrons where Limited Training Data are Available for Some Classes, 9th International Conference on Artificial Neural Networks (ICANN99), Edinburgh, IEE Conference publication No. 470, pp. 227-232.
[52] 焦李成. 神经网络系统理论. 西安电子科技大学出版社,1990.12-32.