多通道振动信号与滑油屑末信息融合的滚动轴承状态监控方法  

Multi-channel vibration signal and debris particle information fusion forrolling bearing condition monitoring method

作　　者：栾孝驰 白天 赵俊豪 沙云东 雷志浩 Luan Xiaochi;Bai Tian;Zhao Junhao;Sha Yundong;Lei Zhihao(Key Laboratory of Advanced Measurement and Test Technique for Aviation Propulsion System,Liaoning Province,School of Aero-Engine,Shenyang Aerospace University,Shenyang 110136,China)

机构地区：[1]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,沈阳110136

出　　处：《仪器仪表学报》2025年第1期298-310,共13页Chinese Journal of Scientific Instrument

基　　金：辽宁省教育厅基本科研项目(JYTMS20230249)资助。

摘　　要：针对单一检测手段难以对航空发动机主轴承进行状态监测以及准确诊断故障的问题,提出多通道振动信号与滑油屑末信息融合的滚动轴承状态监控方法。该方法首先通过建立的多通道振动信息加权融合模型将多个振动传感器测得的数据进行加权融合,然后利用CEEMDAN对融合后的信号进行分解,根据峭度-相关系数筛选准则筛选出强冲击性分量进行重构,得到一个富含轴承故障特征信息的振动信号;再选用总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;通过选取隶属度函数,根据实际情况及专家经验定义模糊推理规则,基于模糊推理理论将总有效值和特征能量进行第1次融合为振动信息参数F1;然后将测得的滑油金属屑末数作为剥落屑末信息参数F2,再基于模糊推理理论将F1与F2进行第2次融合分析;最后监测滚动轴承状态并诊断轴承故障。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。结果表明,多通道振动信号与滑油屑末信息融合的滚动轴承状态监控方法可进行故障特征综合分析并有效判别轴承运行状态。In response to the challenge of difficulty in monitoring and accurately diagnosing the state of main bearings in aircraft engines using a single detection method,a method for rolling bearing condition monitoring is proposed,integrating multi-channel vibration signals with oil debris particle information.This approach initially utilizes a weighted fusion model for multi-channel vibration information to combine data obtained from multiple vibration sensors.Subsequently,the fused signal is decomposed using CEEMDAN,and components with strong impact characteristics are selected based on kurtosis-correlation coefficient filtering criteria,leading to the reconstruction of a vibration signal rich in bearing fault characteristic information.Time-domain features,using the total effective value,and frequency-domain features,employing feature energy,are then chosen as characteristic parameters.Through the selection of membership functions and the definition of fuzzy inference rules based on practical considerations and expert experience,fuzzy inference theory is applied to fuse the total effective value and feature energy into the first-level fused vibration information parameter,denoted as F 1.The obtained oil metal debris particle count is utilized as the information parameter F 2 for debris,which is further analyzed through a second-level fusion using fuzzy inference theory.Finally,the rolling bearing status is monitored,and bearing faults are diagnosed.Experimental tests involving the shedding and expansion of main bearing debris in aircraft engines are conducted.A vibration and oil debris particle detection system is installed to simultaneously collect vibration and oil debris particle information throughout the entire bearing shedding process.The proposed method is applied to analyze the collected data.Results indicate that the multi-channel vibration signal and oil debris particle information fusion method for rolling bearing condition monitoring enables comprehensive analysis of fault characteristics and effective di

关 键 词：滚动轴承 振动信号 滑油金属屑末 多通道信息融合 峭度-相关系数准则 状态监控 航空发动机 

分 类 号：TH133[机械工程—机械制造及自动化]