| 赵俊豪,沙云东,栾孝驰,刘明国.基于振动与滑油信息决策融合的航空发动机主轴承状态监控方法[J].装备环境工程,2024,21(9):34-41. ZHAO Junhao,SHA Yundong,LUAN Xiaochi,LIU Mingguo.Condition Monitoring of Aero-engine Main Bearings Based on Decision Fusion of Vibration and Oil Information[J].Equipment Environmental Engineering,2024,21(9):34-41. |
| 基于振动与滑油信息决策融合的航空发动机主轴承状态监控方法 |
| Condition Monitoring of Aero-engine Main Bearings Based on Decision Fusion of Vibration and Oil Information |
| 投稿时间：2024-08-11 修订日期：2024-09-03 |
| DOI：10.7643/issn.1672-9242.2024.09.005 |
| 中文关键词: 滚动轴承 振动信号 滑油金属屑末 决策融合 状态监控 模糊推理 航空发动机中图分类号:V231.92 文献标志码:A 文章编号:1672-9242(2024)09-0034-08 |
| 英文关键词:rolling bearing vibration signal oil metal debris decision fusion condition monitoring fuzzy reasoning aero-engine |
| 基金项目:中国航发产学研合作项目(HFZL2018CXY017) |
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| Author | Institution |
| ZHAO Junhao | School of Aero-engine, Shenyang Aerospace University, Shenyang 110136, China |
| SHA Yundong | School of Aero-engine, Shenyang Aerospace University, Shenyang 110136, China |
| LUAN Xiaochi | School of Aero-engine, Shenyang Aerospace University, Shenyang 110136, China |
| LIU Mingguo | School of Aero-engine, Shenyang Aerospace University, Shenyang 110136, China |
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| 中文摘要: |
| 目的 解决实际工作条件下航空发动机滚动轴承运行状态在线监测及故障诊断问题。方法 首先选用有效值作为时域特征参数，提出特征能量作为频域特征参数，与滑油金属屑末数作为融合的振动及滑油屑末信息。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，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 随故障扩展，振动信号有效值参数为总体上升趋势。频域特征能量随故障扩展升高到一定程度后下降，并产生波动，对轴承早期故障诊断较为敏感。滑油屑末为诊断轴承故障的重要信息，其变化趋势为单调递增，轴承故障后期，滑油屑末信息变化较为显著，对轴承后期的故障诊断较为敏感。结论 基于模糊推理理论的振动和滑油屑末信息融合方法可将不同信号进行故障特征综合分析，并可有效判别轴承的运行状态。 |
| 英文摘要: |
| The work aims to solve the problem of on-line monitoring and fault diagnosis of aero-engine rolling bearing under actual working conditions. Firstly, the effective value was selected as the time domain characteristic parameter, the characteristic energy was proposed as the frequency domain characteristic parameter, and the number of oil metal debris was used as the fusion vibration and oil debris information. The above parameters were fused based on fuzzy inference theory. By selecting membership functions and defining fuzzy inference rules, the vibration signals and oil metal debris information were fused to diagnose bearing faults. The spalling extension test of the aero-engine main bearing was carried out, the vibration and oil debris detection system was installed, the vibration and oil debris information of the bearing in the whole process of spalling was collected synchronously, and the measured data were analyzed by the proposed method.With the fault expansion, the effective value of vibration signal parameters was an overall upward trend. The frequency domain characteristic energy decreased and fluctuated with the increase of fault spread to a certain extent, which was sensitive to early fault diagnosis of the bearing. Oil debris was the important information for bearing fault diagnosis, and its change trend was monotonically increasing. Oil debris information changed significantly in the later stage of bearing fault, which was sensitive to bearing fault diagnosis. The vibration and oil debris information fusion method based on fuzzy reasoning theory can comprehensively analyze the fault characteristics of different signals and effectively distinguish the running state of bearings. |
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