基于振动和滑油屑末信息融合的主轴承状态监测方法  

Main Bearing Condition Monitoring Method Based on Vibration and Oil Debris Information Fusion

作　　者：郭小鹏 杜少辉 安中彦 杨杰 沙云东 栾孝驰 GUO Xiao-peng;DU Shao-hui;AN Zhong-yan;YANG Jie;SHA Yun-dong;LUAN Xiao-chi(AECC Shenyang Engine Research Institute,Shenyang 110015,China;School of Aero-Engine,S henyang Aerospace University,Shenyang 110136,China;Liaoning Key Laboratory of Advanced Measurement and Test Technology for Aircraft Propulsion System,Shenyang 110136,China)

机构地区：[1]中国航发沈阳发动机研究所,沈阳110015 [2]沈阳航空航天大学航空发动机学院,沈阳110136 [3]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：《航空发动机》2025年第1期140-145,共6页Aeroengine

基　　金：航空动力基础研究项目资助。

摘　　要：针对实际服役状态下航空发动机主轴承在线状态监测和故障诊断问题,提出了一种基于振动和滑油屑末信息融合的主轴承状态监测方法。通过发动机外机匣指定位置处测得振动信号的频域特征定义滚动轴承故障损伤因子,采集回油路滑油金属屑末信息确定屑末数量增长率,将二者通过模糊推理相融合,实现滚动轴承状态的在线监测。开展了部件试验器条件下航空发动机主轴承典型损伤剥落扩展试验和整机试验,同步测试振动信号及滑油屑末信息,进行信息融合轴承状态监测方法验证。结果表明:将轴承剥落中期时的振动和滑油屑末信息输入建立的模糊推理模型,输出结果为0.59,根据定义输出数值在0~0.25区间表示轴承状态良好,输出数值在0.25~0.75区间表示轴承状态异常,输出数值在0.75~1区间表示轴承故障严重,该数值置于轴承状态异常区间,据此判断此时轴承存在故障、需及时检修。所提方法可为航空发动机主轴轴承状态监测及故障诊断提供参考。Aiming at the problem of online condition monitoring and fault diagnosis of aeroengine main bearings under actual service conditions,a method of main bearing condition monitoring based on information fusion of vibration and oil particles was proposed.The frequency domain characteristics of the vibration signal measured at the designated position of the engine outer casing are used to define the rolling bearing fault damage factor,and the metal debris information of the oil return circuit is collected to determine the growth rate of the debris quantity.The two are combined through fuzzy reasoning to achieve online monitoring of the rolling bearing status.The typical damage and flaking propagation test of the main bearing of an aeroengine under the condition of a component tester and the entire machine test were carried out,and the vibration signal and oil debris information were synchronously tested,and the information fusion bearing condition monitoring method was validated.The vibration and oil debris information during the mid-stage of bearing spalling were input into the fuzzy inference model established in this paper,yielding an output of 0.59.According to the defined criteria,an output value in the range of 0–0.25 indicates the bearing is in good condition,0.25–0.75 indicates an abnormal condition,and 0.75–1 signifies a severe fault.Since the output value falls within the abnormal condition range,it is determined that the bearing is faulty and requires timely maintenance.The proposed method provides a reference for the condition monitoring and fault diagnosis of main bearings in aircraft engines.

关 键 词：滚动轴承 状态监测 振动信号 滑油屑末 模糊推理 

分 类 号：V233[航空宇航科学与技术—航空宇航推进理论与工程]