Integration of vibration and lubricating metal particle information for condition monitoring of aircraft engine main bearings
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摘要:
针对单一检测手段难以对航空发动机主轴承进行在线监测以及准确诊断故障的问题，提出一种振动与滑油金属屑末信息融合的航空发动机主轴承状态监控方法。首先选用有效值作为时域特征参数，定义特征能量作为频域特征参数，滑油金属屑末数作为滑油屑末信息。基于模糊推理理论将上述参数进行融合，通过选取隶属度函数、定义模糊推理规则，进行振动信号及滑油金属屑末信息的融合分析诊断轴承故障。开展航空发动机主轴承剥落扩展试验及某型航空发动机整机试车试验，安装振动及滑油屑末检测系统，同步采集轴承剥落全程的振动及滑油屑末信息，并应用所提出方法对所测得数据进行分析。结果表明：轴承运行1 h，计算得到输出轴承状态数值为0.18，处于0～0.35之间，轴承状态良好；运行中期9 h，轴承状态数值为0.5，处于0.35～0.65之间，轴承状态欠佳；轴承运行后期18 h，计算得到输出轴承状态数值为0.82，处于0.65～1之间，轴承故障严重。所提信息融合方法可有效监测轴承运行状态，可为航空发动机主轴承状态监控提供有效手段。
Abstract:To address the challenge of online monitoring and accurate fault diagnosis of the main bearings in aircraft engines using a single detection method, a state monitoring approach based on the fusion of vibration signals and oil debris information was proposed. First, the root mean square value was selected as the time-domain feature parameter, feature energy was defined as the frequency-domain feature parameter, and the number of metal debris particles in the oil was used as the oil debris information. Based on fuzzy inference theory, these parameters were fused by selecting membership functions and defining fuzzy inference rules to perform fusion analysis and fault diagnosis of the bearings using vibration signals and oil debris information. The results indicated that after operating 1 hour, the calculated bearing condition value was 0.18, which fell within the range of 0 to 0.35, signifying that the bearing was in good condition. After medium-term operation of 9 hours, the bearing condition value was 0.5, within the range of 0.35 to 0.65, indicating a suboptimal bearing state. Running towards the end of its operation of 18 hours, the calculated bearing condition value was 0.82, within the range of 0.65 to 1, suggesting severe bearing failure. The proposed information fusion method can effectively monitor the operational status of the bearing and can provide an effective means for the condition monitoring of the main bearing in an aircraft engine.
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图 5 航空发动机主轴承剥落故障扩展试验台
Figure 5. Aircraft engine main bearing spalling fault extension test bench
图 6 航空发动机部件试验器主轴承振动及滑油屑末采集系统示意图
Figure 6. aircraft engine component tester main bearing vibration and lubricating metal particle debris collection system diagram
图 7 轴承运行1 h振动时域信号
Figure 7. Vibration time domain signal of bearing after1 h operation
图 8 轴承运行9 h振动时域信号
Figure 8. Vibration time domain signal of bearing after9 h operation
图 9 轴承运行18 h振动时域信号
Figure 9. Vibration time domain signal of bearing after18 h operation
图 10 运行全程时域参数、滑油金属屑末信息变化趋势及轴承损伤情况
Figure 10. Time domain parameters of the whole running process, the changing trend of lubricating metal particle debris information and the bearing damage situation
图 11 轴承运行1 h小波包分解筛选指数
Figure 11. Bearing run 1 h wavelet packet decomposition screening index
图 12 轴承运行1 h振动信号频域信息
Figure 12. Frequency domain information after vibration signal of bearing running for 1 h
图 13 轴承运行9 h振动信号频域信息
Figure 13. Frequency domain information after vibration signal of bearing running for 9 h
图 14 轴承运行18 h振动信号频域信息
Figure 14. Frequency domain information after vibration signal of bearing running for 18 h
图 15 运行全程特征能量及滑油屑末信息变化
Figure 15. Characteristic energy and lubricating metal particle debris information change throughout the operation
图 16 发动机主轴承振动和滑油金属屑末信息融合诊断测试系统
Figure 16. Engine main bearing vibration and lubricating metal particle debris information fusion diagnostic test system
图 17 时域信号振动有效值梯形隶属度函数
Figure 17. Trapezoidal membership function of time domain signal RMS
图 18 频域信号特征能量广义钟形隶属度函数
Figure 18. Generalized bell membership function of signal characteristic energy in frequency domain
图 21 有效值-滑油屑末数模糊推理规则
Figure 21. RMS-lubricating metal particle debris number fuzzy inference rule
图 22 特征能量-滑油屑末数模糊推理规则
Figure 22. Characteristic energy-lubricating metal particle debris number fuzzy inference rule
图 23 运行1 h轴承状态模糊推理逻辑图
Figure 23. Fuzzy reasoning logic diagram of bearing status after 1 h of operation
图 24 运行9 h轴承状态模糊推理逻辑图
Figure 24. Fuzzy reasoning logic diagram of bearing status after 9 h of operation
图 25 运行18 h轴承状态模糊推理逻辑图
Figure 25. Fuzzy reasoning logic diagram of bearing status after 18 h of operation
图 26 轴承运行全程故障诊断及状态识别
Figure 26. Bearing operation fault diagnosis and status recognition
表 1 金属屑末单次增长量
Table 1. Single increase of metal particle debris
运行
时间/h剥落尺寸（长×宽）/（mm×mm） 增长量 试验前 试验后 2 8×6 15×12 850～900 4 15×12 23×17 850～900 6 23×17 25×17 750～800 10 25×17 25×20 600～650 14 25×20 31×20 约 1000 18 31×20 70×20 约 4000 
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表 2 轴承运行全程时域参数变化
Table 2. Time domain parameter change of bearing operation
运行时间/h 有效值 峰值 滑油金属屑末数 1 27.2g 109.3g 450 2 12.9g 52.5g 900 3 22.8g 74.7g 1350 4 45.2g 163.6g 1800 5 24.9g 111.8g 2200 6 33.9g 137.6g 2600 7 25.7g 98.4g 2760 8 35.3g 123.6g 2925 9 31.8g 113.7g 3085 10 26.6g 104.6g 3250 11 32.6g 126.2g 3500 12 33.6g 146.8g 3750 13 40.9g 141.6g 4000 14 52.9g 165.3g 4250 15 54.1g 219.5g 5250 16 88.9g 389.6g 6250 17 112.9g 417.1g 7250 18 106.9g 405.3g 8250
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表 3 轴承运行1 h小波包节点分量参数
Table 3. Parameter of node component of wavelet packet in bearing operation for 1 h
节点 偏度 峭度 排列熵 1 1.00 0.49 0.53 2 0.18 0.48 0.69 3 0.01 0.94 0.85 4 0.79 0.79 0.87 5 0.00 0.63 0.71 6 0.02 0.69 0.89 7 0.02 1.00 0.91 8 0.10 0.69 1.00
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表 4 轴承运行全程特征能量变化趋势
Table 4. Characteristic energy variation trend of bearing operation
运行时间/h 特征能量 滑油金属屑末数 1 85.5 450 2 120.3 900 3 138.0 1350 4 173.7 1800 5 235.0 2200 6 205.0 2600 7 219.8 2760 8 302.3 2925 9 291.0 3085 10 171.1 3250 11 200.7 3500 12 232.6 3750 13 83.6 4000 14 140.2 4250 15 176.3 5250 16 83.1 6250 17 87.9 7250 18 170.6 8250
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表 5 轴承运行全程损伤程度判别
Table 5. Judging the damage degree of the whole bearing operation
运行
时间/h有效值 特征
能量滑油金属
屑末数轴承
状态1 27.2g 85.5 450 良好 2 12.9g 120.3 900 良好 3 22.8g 138.0 1350 欠佳 4 45.2g 173.7 1800 欠佳 5 24.9g 235.0 2200 欠佳 6 33.9g 205.0 2600 欠佳 7 25.7g 219.8 2760 欠佳 8 35.3g 302.3 2925 欠佳 9 31.8g 291.0 3085 欠佳 10 26.6g 171.1 3250 欠佳 11 32.6g 200.7 3500 欠佳 12 33.6g 232.6 3750 欠佳 13 40.9g 83.6 4000 严重 14 52.9g 140.2 4250 严重 15 54.1g 176.3 5250 严重 16 88.9g 83.1 6250 严重 17 112.9g 87.9 7250 严重 18 106.9g 170.6 8250 严重
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[1] 尉询楷,冯悦,杨立,等. 航空发动机中介主轴承故障预测研究[C]//航空安全与装备维修技术——航空安全与装备维修技术学术研讨会论文集,北京: 国防工业出版社,2014. WEI Xunkai,FENG Yue,YANG Li,et al. Research on Fault Prediction of aero-engine Intermediate Main bearing [C]//Aviation Safety and Equipment Maintenance Technology—Proceedings of the Symposium on Aviation Safety and Equipment Maintenance Technology,Beijing: National Defence Industry Press,2014. (in ChineseWEI Xunkai, FENG Yue, YANG Li, et al. Research on Fault Prediction of aero-engine Intermediate Main bearing [C]//Aviation Safety and Equipment Maintenance Technology—Proceedings of the Symposium on Aviation Safety and Equipment Maintenance Technology, Beijing: National Defence Industry Press, 2014. (in Chinese) [2] 岳晓晶,刘廷武,李锟. 航空发动机轴承性能试验中磁悬浮电动机失稳现象分析[J]. 轴承,2022(3): 86-90. YUE Xiaojing,LIU Tingwu,LI Kun. Instability analysis of maglev motor during aero-engine bearing performance test[J]. Bearing,2022(3): 86-90. (in ChineseYUE Xiaojing, LIU Tingwu, LI Kun. Instability analysis of maglev motor during aero-engine bearing performance test[J]. Bearing, 2022(3): 86-90. (in Chinese) [3] 贾艳秋,张兵,陈雪梅. 滚动轴承的故障机理及诊断[J]. 化工装备技术,2011,32(4): 55-57. JIA Yanqiu,ZHANG Bing,CHEN Xuemei. Malfunction mechanism and diagnosis of rolling bearing[J]. Chemical Equipment Technology,2011,32(4): 55-57. (in Chinese doi: 10.3969/j.issn.1007-7251.2011.04.015JIA Yanqiu, ZHANG Bing, CHEN Xuemei. Malfunction mechanism and diagnosis of rolling bearing[J]. Chemical Equipment Technology, 2011, 32(4): 55-57. (in Chinese) doi: 10.3969/j.issn.1007-7251.2011.04.015 [4] 张利慧,李殊瑶,李晓波,等. 基于时频域特征参数的风电机组滚动轴承故障诊断方法及应用[J]. 内蒙古电力技术,2024,42(3): 13-19. ZHANG Lihui,LI Shuyao,LI Xiaobo,et al. Diagnostic method of wind turbine rolling bearing failure based on time-frequency domain characteristic parameters and its application[J]. Inner Mongolia Electric Power,2024,42(3): 13-19. (in ChineseZHANG Lihui, LI Shuyao, LI Xiaobo, et al. Diagnostic method of wind turbine rolling bearing failure based on time-frequency domain characteristic parameters and its application[J]. Inner Mongolia Electric Power, 2024, 42(3): 13-19. (in Chinese) [5] 王仲,姜娇,张磊,等. 时间特征与空间特征融合的轻量网络故障诊断方法[J]. 机电工程,2024,41(9): 1565-1574. WANG Zhong,JIANG Jiao,ZHANG Lei,et al. Lightweight network fault diagnosis method combining temporal features with spatial features[J]. Journal of Mechanical & Electrical Engineering,2024,41(9): 1565-1574. (in Chinese doi: 10.3969/j.issn.1001-4551.2024.09.005WANG Zhong, JIANG Jiao, ZHANG Lei, et al. Lightweight network fault diagnosis method combining temporal features with spatial features[J]. Journal of Mechanical & Electrical Engineering, 2024, 41(9): 1565-1574. (in Chinese) doi: 10.3969/j.issn.1001-4551.2024.09.005 [6] 崔凯,张海峰,曲松,等. 基于时域分析法的牵引电机风机轴承性能评估[J]. 佳木斯大学学报(自然科学版),2019,37(2): 277-280. CUI Kai,ZHANG Haifeng,QU Song,et al. Performance evaluation of traction motor fan bearing based on time domain Analysis[J]. Journal of Jiamusi University (Natural Science Edition),2019,37(2): 277-280.CUI Kai, ZHANG Haifeng, QU Song, et al. Performance evaluation of traction motor fan bearing based on time domain Analysis[J]. Journal of Jiamusi University (Natural Science Edition), 2019, 37(2): 277-280. [7] LUAN Xiaochi,ZHONG Chenghao,ZHAO Fengtong,et al. Bearing fault damage degree identification method based on SSA-VMD and Shannon entropy-exponential entropy decision[J]. Structural Health Monitoring,2024,23(5): 3105-3133. doi: 10.1177/14759217231219710 [8] 栾孝驰,赵俊豪,沙云东,等. 基于循环提取有效信息的主轴承故障特征增强方法[J]. 仪器仪表学报,2024,45(3): 251-262. LUAN Xiaochi,ZHAO Junhao,SHA Yundong,et al. A main bearing fault feature enhancement method based on cyclical information extraction[J]. Chinese Journal of Scientific Instrument,2024,45(3): 251-262. (in ChineseLUAN Xiaochi, ZHAO Junhao, SHA Yundong, et al. A main bearing fault feature enhancement method based on cyclical information extraction[J]. Chinese Journal of Scientific Instrument, 2024, 45(3): 251-262. (in Chinese) [9] 栾孝驰,赵俊豪,沙云东,等. 基于特征能量建立的航空发动机滚动轴承故障预警方法[J/OL]. 航空动力学报,(2024-07-09) [2025-02-25]. https://doi.org/10.13224/j.cnki.jasp.20240094. LUAN Xiaochi,ZHAO Junhao,SHA Yundong,et al. Aero engine rolling bearing fault warning method based on characteristic energy [J/OL]. Journal of Aerospace Power,(2024-07-09) [2025-02-25]. https://doi.org/10.13224/j.cnki.jasp.20240094. (in ChineseLUAN Xiaochi, ZHAO Junhao, SHA Yundong, et al. Aero engine rolling bearing fault warning method based on characteristic energy [J/OL]. Journal of Aerospace Power, (2024-07-09) [2025-02-25]. https://doi.org/10.13224/j.cnki.jasp.20240094. (in Chinese) [10] SHA Yundong,ZHAO Junhao,LUAN Xiaochi,et al. Fault feature signal extraction method for rolling bearings in gas turbine engines based on threshold parameter decision screening[J]. Measurement,2024,231: 114567. doi: 10.1016/j.measurement.2024.114567 [11] 黄慧杰. 自适应时频域分析法在滚动轴承早期故障诊断中的研究[D]. 包头: 内蒙古科技大学,2019. HUANG Huijie. Research on adaptive time-frequency domain analysis method in early fault diagnosis of rolling bearings[D]. Baotou: Inner Mongolia University of Science & Technology,2019. (in ChineseHUANG Huijie. Research on adaptive time-frequency domain analysis method in early fault diagnosis of rolling bearings[D]. Baotou: Inner Mongolia University of Science & Technology, 2019. (in Chinese) [12] 赵江涛. 基于包络谱分析的振动信号研究及其FPGA实现[D]. 西安: 西安理工大学,2017. ZHAO Jiangtao. Research on vibration signal based on envelope spectrum analysis and its FPGA implementation[D]. Xi’an: Xi’an University of Technology,2017. (in ChineseZHAO Jiangtao. Research on vibration signal based on envelope spectrum analysis and its FPGA implementation[D]. Xi’an: Xi’an University of Technology, 2017. (in Chinese) [13] 赵俊峰,邹金兰. 故障轴承振动信号的小波包络谱分析[J]. 中国科技信息,2006(6): 131,136. ZHAO Junfeng,ZOU Jinlan. Wavelet envelope spectrum analysis of vibration signal of fault bearing[J]. China Science and Technology Information,2006(6): 131,136. (in ChineseZHAO Junfeng, ZOU Jinlan. Wavelet envelope spectrum analysis of vibration signal of fault bearing[J]. China Science and Technology Information, 2006(6): 131, 136. (in Chinese) [14] XU Huoyao,LIU Jie,PENG Xiangyu,et al. Deep dynamic adaptation network: a deep transfer learning framework for rolling bearing fault diagnosis under variable working conditions[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering,2022,45(1): 41. [15] YE Yangfei,ZHANG Meng. Bearing fault diagnosis model using improved Bayesian information criterion-based variational modal decomposition and IGA-SVM[J]. Advances in Mechanical Engineering,2022,14(12): 168781322211421. [16] WANG Chengjie,BAI Chenzhao,YANG Zhaoxu,et al. Research on high sensitivity oil debris detection sensor using high magnetic permeability material and coil mutual inductance[J]. Sensors,2022,22(5): 1833. doi: 10.3390/s22051833 [17] BOWEN E R,WESTCOTT V C. Wear particle atlas: N00156- 74-C-1682 [R]. Burlington: Foxboro/Trans-Sonics. 1976. [18] ANDERSON D P. Wear particle atlas. revised[R]. Burlington,US: Foxboro Analytical,1982. [19] 杨景来,邢春生,佟文伟,等. 基于磨粒分析的球轴承及转子组件转动微动磨损特性研究[J/OL]. 航空动力学报,(2024-12-12)[2025-02-25]. https://doi.org/10.13224/j.cnki.jasp.20240584. YANG Jinglai,XING Chunsheng,TONG Wenwei,et al. Research on rotational fretting wear characteristics of ball bearing and rotor Assembly based on wear particle analysis [J/OL]. Journal of Aerospace Power,(2024-12-12) [2025-02-25]. https://doi.org/10.13224/j.cnki.jasp.20240584. (in ChineseYANG Jinglai, XING Chunsheng, TONG Wenwei, et al. Research on rotational fretting wear characteristics of ball bearing and rotor Assembly based on wear particle analysis [J/OL]. Journal of Aerospace Power, (2024-12-12) [2025-02-25]. https://doi.org/10.13224/j.cnki.jasp.20240584. (in Chinese) [20] 沙云东,赵宇,栾孝驰,等. 基于多参数信息融合筛选的滚动轴承振动信号特征提取与表征方法[J]. 推进技术,2023,44(7): 2205050. SHA Yundong,ZHAO Yu,LUAN Xiaochi,et al. Feature extraction and characterization of rolling bearing vibration signal based on multi parameter information fusion and screening[J]. Journal of Propulsion Technology,2023,44(7): 2205050. (in ChineseSHA Yundong, ZHAO Yu, LUAN Xiaochi, et al. Feature extraction and characterization of rolling bearing vibration signal based on multi parameter information fusion and screening[J]. Journal of Propulsion Technology, 2023, 44(7): 2205050. (in Chinese) -
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