基于声发射信号信息熵距的滑动轴承润滑状态诊断
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摘要
为了有效判断滑动轴承润滑状态,防止滑动轴承故障引起的重大事故,提出一种基于信息熵和信息熵距的滑动轴承润滑状态诊断方法。通过300 MW汽轮机发电机组转子试验台对滑动轴承的干摩擦、边界摩擦和液体摩擦3种润滑状态进行了模拟,并获取其声发射信号。利用信息熵距方法分析这些声发射信号,通过信息熵距图有效区分滑动轴承的3种润滑状态,保证了滑动轴承的运行性能和安全性。结果表明:加入时-频联合域的信息熵距诊断方法在诊断准确性上要优于仅加入时域和频域的熵距方法。
To effectively evaluate the lubricated state of a sliding bearing and prevent serious accidents caused by sliding bearing faults, a diagnosis method was proposed for the lubrication state of a sliding bearing based on information entropy and information entropy distance. The dry friction state, boundary friction state and liquid friction state of the sliding bearing were simulated on a 300 MW turbo-generator test rig, and subsequently acoustic emission signals of different friction states were obtained, which were then analyzed to identify the lubrication state using the information entropy distance method, so as to guarantee the operation performance and safety of the sliding bearing. Results show that the information entropy distance diagnosis method with wavelet space feature spectrum entropy is more accurate than that without wavelet space feature spectrum entropy.
To effectively evaluate the lubricated state of a sliding bearing and prevent serious accidents caused by sliding bearing faults, a diagnosis method was proposed for the lubrication state of a sliding bearing based on information entropy and information entropy distance. The dry friction state, boundary friction state and liquid friction state of the sliding bearing were simulated on a 300 MW turbo-generator test rig, and subsequently acoustic emission signals of different friction states were obtained, which were then analyzed to identify the lubrication state using the information entropy distance method, so as to guarantee the operation performance and safety of the sliding bearing. Results show that the information entropy distance diagnosis method with wavelet space feature spectrum entropy is more accurate than that without wavelet space feature spectrum entropy.
引文
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[18] 肖钟捷. 基于小波空间特征谱熵的数字图像识别[J]. 吉林大学学报(工学版), 2015, 45(6): 1994-1998. XIAO Zhongjie. Recognition of digital image based on wavelet space feature spectrum entropy[J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(6): 1994-1998.
[19] 陈非, 黄树红, 张燕平, 等. 基于信息熵距的旋转机械振动故障诊断方法[J]. 振动、测试与诊断, 2008, 28(1): 9-13. CHEN Fei, HUANG Shuhong, ZHANG Yanping, et al. Research on diagnosis of vibration faults for rotating machinery based on distance of information entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2008, 28(1): 9-13.
[20] 艾延廷, 付琪, 田晶, 等. 基于融合信息熵距的转子裂纹-碰摩耦合故障诊断方法[J]. 航空动力学报, 2013, 28(10): 2161-2166. AI Yanting, FU Qi, TIAN Jing, et al. Diagnosis method for crack-rubbing coupled fault in rotor system based on integration of information entropy distance[J]. Journal of Aerospace Power, 2013, 28(10): 2161-2166.
[21] SU Houjun, SHI Tielin, CHEN Fei, et al. New method of fault diagnosis of rotating machinery based on distance of information entropy[J]. Frontiers of Mechanical Engineering, 2011, 6(2): 249-253.
[22] 朱益军, 李录平, 靳攀科, 等. 滑动轴承润滑状态与声发射信号特征关系研究[J]. 汽轮机技术, 2011, 53(1): 50-52, 55. ZHU Yijun, LI Luping, JIN Panke, et al. Research on the relationship between characteristic of AE signal and hydrodynamic bearing fault[J]. Turbine Technology, 2011, 53(1): 50-52, 55.
[23] 卢绪祥, 苏一鸣, 吴家腾, 等. 基于EMD及灰色关联度的滑动轴承润滑状态故障诊断研究[J]. 动力工程学报, 2016, 36(1): 42-47. LU Xuxiang, SU Yiming, WU Jiateng, et al. Fault diagnosis on lubrication state of journal bearings based on EMD and grey relational degree[J]. Journal of Chinese Society of Power Engineering, 2016, 36(1): 42-47.
[2] MBA D, RAO R B K N. Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines; bearings, pumps, gearboxes, engines and rotating structures[J]. The Shock and Vibration Digest, 2006, 38(1): 3-16.
[3] 朱益军. 基于声发射检测的滑动轴承状态诊断技术研究[D]. 长沙: 长沙理工大学, 2011.
[4] 黄琪. 基于声发射检测的滑动轴承故障诊断方法研究[D]. 长沙: 长沙理工大学, 2008.
[5] TOWSYFYAN H, RAHARJO P, GU F, et al. Characterization of acoustic emissions from journal bearings for fault detection[J]. University of Huddersfield Repository, 2013, 1(1): 13.
[6] 陆利威, 李新春, 张田. 基于声发射技术的滑动轴承故障诊断方法研究[J]. 机械工程与自动化, 2010(6): 123-124, 127. LU Liwei, LI Xinchun, ZHANG Tian. Fault diagnosis method based on acoustic emission technique for journal bearings[J]. Mechanical Engineering & Automation, 2010(6): 123-124, 127.
[7] 张颖, 樊瑞筱, 丛蕊, 等. 基于声发射波形流信号的滑动轴承故障特征双谱分析[J]. 轴承, 2016(6): 47-50, 62. ZHANG Ying, FAN Ruixiao, CONG Rui, et al. Bispectrum analysis for fault characteristics of sliding bearings based on acoustic emission waveform streaming signals[J]. Bearing, 2016(6): 47-50, 62.
[8] 王晓伟, 刘占生, 张广辉, 等. 基于声发射的可倾瓦径向滑动轴承碰摩故障诊断[J]. 中国电机工程学报, 2009, 29(8): 64-69. WANG Xiaowei, LIU Zhansheng, ZHANG Guanghui, et al. Rubbing fault diagnose of tilting pad journal bearing by acoustic emission[J]. Proceedings of the CSEE, 2009, 29(8): 64-69.
[9] 贾华龙. 声发射在机械结构缺陷检测中的应用[D]. 昆明: 昆明理工大学, 2014.
[10] 徐瑞利. 基于信息熵的滚动轴承声发射信号故障诊断[D]. 兰州: 兰州理工大学, 2012.
[11] 关焦月, 艾延廷, 田晶, 等. 基于融合信息熵距的滚动轴承故障诊断方法[J]. 沈阳航空航天大学学报, 2015, 32(4): 14-18. GUAN Jiaoyue, AI Yanting, TIAN Jing, et al. Diagnosis method for rolling bearing faults based on integration of information entropy distance[J]. Journal of Shenyang Aerospace University, 2015, 32(4): 14-18.
[12] 艾延廷, 费成巍. 转子振动故障的小波能谱熵SVM诊断方法[J]. 航空动力学报, 2011, 26(8): 1830-1835. AI Yanting, FEI Chengwei. Rotor vibration fault diagnosis method based on wavelet energy spectrum entropy and SVM[J]. Journal of Aerospace Power, 2011, 26(8): 1830-1835.
[13] 卢文祥, 杜润生. 工程测试与信息处理[M]. 2版. 武汉: 华中科技大学出版社, 2002.
[14] 申弢, 黄树红, 韩守木, 等. 旋转机械振动信号的信息熵特征[J]. 机械工程学报, 2001, 37(6): 94-98. SHEN Tao, HUANG Shuhong, HAN Shoumu, et al. Extracting information entropy features for rotating machinery vibration signals[J]. Chinese Journal of Mechanical Engineering, 2001, 37(6): 94-98.
[15] 陈非. 基于过程信息融合的旋转机械信息(火用)故障诊断研究[D]. 武汉: 华中科技大学, 2010.
[16] CUI H X, ZHANG L B, KANG R Y, et al. Research on fault diagnosis for reciprocating compressor valve using information entropy and SVM method[J]. J Loss Prev Process Ind, 2009,22(6): 864-867.
[17] 陈非, 黄树红, 张燕平, 等. 火电机组信息融合故障诊断方法及其发展[J]. 振动、测试与诊断, 2005, 25(1): 17-21. CHEN Fei, HUANG Shuhong, ZHANG Yanping, et al. Methodology and development of fault diagnosis based on information fusion in power generation unit[J]. Journal of Vibration, Measurement & Diagnosis, 2005, 25(1): 17-21.
[18] 肖钟捷. 基于小波空间特征谱熵的数字图像识别[J]. 吉林大学学报(工学版), 2015, 45(6): 1994-1998. XIAO Zhongjie. Recognition of digital image based on wavelet space feature spectrum entropy[J]. Journal of Jilin University (Engineering and Technology Edition), 2015, 45(6): 1994-1998.
[19] 陈非, 黄树红, 张燕平, 等. 基于信息熵距的旋转机械振动故障诊断方法[J]. 振动、测试与诊断, 2008, 28(1): 9-13. CHEN Fei, HUANG Shuhong, ZHANG Yanping, et al. Research on diagnosis of vibration faults for rotating machinery based on distance of information entropy[J]. Journal of Vibration, Measurement & Diagnosis, 2008, 28(1): 9-13.
[20] 艾延廷, 付琪, 田晶, 等. 基于融合信息熵距的转子裂纹-碰摩耦合故障诊断方法[J]. 航空动力学报, 2013, 28(10): 2161-2166. AI Yanting, FU Qi, TIAN Jing, et al. Diagnosis method for crack-rubbing coupled fault in rotor system based on integration of information entropy distance[J]. Journal of Aerospace Power, 2013, 28(10): 2161-2166.
[21] SU Houjun, SHI Tielin, CHEN Fei, et al. New method of fault diagnosis of rotating machinery based on distance of information entropy[J]. Frontiers of Mechanical Engineering, 2011, 6(2): 249-253.
[22] 朱益军, 李录平, 靳攀科, 等. 滑动轴承润滑状态与声发射信号特征关系研究[J]. 汽轮机技术, 2011, 53(1): 50-52, 55. ZHU Yijun, LI Luping, JIN Panke, et al. Research on the relationship between characteristic of AE signal and hydrodynamic bearing fault[J]. Turbine Technology, 2011, 53(1): 50-52, 55.
[23] 卢绪祥, 苏一鸣, 吴家腾, 等. 基于EMD及灰色关联度的滑动轴承润滑状态故障诊断研究[J]. 动力工程学报, 2016, 36(1): 42-47. LU Xuxiang, SU Yiming, WU Jiateng, et al. Fault diagnosis on lubrication state of journal bearings based on EMD and grey relational degree[J]. Journal of Chinese Society of Power Engineering, 2016, 36(1): 42-47.