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基于特征量阈值判决的轴承故障诊断方法
栾孝驰1,2,那万晓2,3,沙云东2,柳贡民1,李壮2,朱林3
1.哈尔滨工程大学 动力与能源工程学院,黑龙江 哈尔滨 150001;2.沈阳航空航天大学 航空发动机学院,辽宁省航空推进系统先进测试技术重点实验室,辽宁 沈阳 110136;3.中国航空发动机集团 中国航发南方工业有限公司,湖南 株洲 412000
摘要:
针对航空发动机在不分解状态下主轴轴承故障诊断难的问题,以振动信号分析和处理为基础,以小波包分解与重构、峭度值指标、频谱分析和包络解调为预处理方式,提出了基于故障轴承和正常轴承的特征倍频能量总和占整个包络谱能量百分比的特征差异确定轴承故障的诊断方法,以凯斯西储大学深沟球轴承典型试验数据验证了该方法的有效性。验证外圈点蚀故障轴承特征倍频能量占比最高可达76%,而正常轴承特征倍频能量占比均低于6.5%。为了研究该方法对复杂传递路径下轴承故障特征的识别效果,搭建了基于模拟机匣的航空发动机主轴轴承试验台,在机匣外部布置测点,对多个转速下的滚动体划伤故障轴承和正常轴承的测试数据进行预处理后应用该诊断方法。结果表明,基于特征量阈值判决的轴承故障诊断方法适用于简单和复杂传递路径下轴承的故障诊断,可为飞行状态下的航空发动机主轴轴承故障诊断和状态监测提供方法参考。
关键词:  航空发动机  轴承  故障诊断  信号分析  状态监测
DOI:10.13675/j.cnki.tjjs.200921
分类号:V235.13
基金项目:国家自然科学基金项目(51579051);辽宁省教育厅系列项目(JYT2020010);中国航发产学研合作项目(HFZL2018CXY017)。
Bearing Fault Diagnosis Method Based on Eigenvalue Threshold Decision
LUAN Xiao-chi1,2, NA Wan-xiao2,3, SHA Yun-dong2, LIU Gong-min1, LI Zhuang2, ZHU Lin3
1.College of Power and Energy Engineering,Harbin Engineering University,Harbin 150001,China;2.Key Laboratory of Advanced Measurement and Test Technique for Aviation Propulsion System,Liaoning Province, School of Aero-Engine,Shenyang Aerospace University,Shenyang 110136,China;3.AECC South Industry Company Limited,Aero Engine Corporation of China,Zhuzhou 412002,China
Abstract:
Aiming at the problem that it is difficult to diagnose the fault of the main shaft bearing in the undecomposed state of aero-engine, based on the analysis and processing of vibration signals, wavelet packet decomposition and reconstruction, kurtosis index, spectrum analysis and envelope demodulation are used as preprocessing methods. A diagnosis method of bearing fault is proposed based on the characteristic difference of the sum of characteristic frequency doubling energy of the fault bearing and the normal bearing in the percentage of energy of the whole envelope spectrum. Firstly, the validity of the proposed method is verified by the typical test data of deep groove ball bearings from Case Western Reserve University. The characteristic frequency doubling energy ratio of the outer ring pitting fault bearing is up to 76%, while the normal bearing is lower than 6.5%. Secondly, in order to study the effectiveness of the method on the fault diagnosis of rolling bearings under complex transmission paths, an aero-engine mainshaft bearing test rig based on the simulated casing was built, and the measuring points were arranged outside the casing. The diagnosis method is applied after preprocessing the test data of the rolling body scratch fault bearing and the normal bearing at multiple rotating speeds. The results show that bearing fault diagnosis method based on eigenvalue threshold decision presented in this paper is suitable for bearing fault diagnosis under simple and complex transmission paths, and it can be used as a reference for fault diagnosis and condition monitoring of aeroengine main shaft bearing in flight status.
Key words:  Aeroengine  Bearing  Fault diagnosis  Signal analysis  Condition monitoring