高速动车组转向架轴承故障监测系统开发与研制
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摘要
轴承损伤会导致车辆异常振动、热轴等,严重影响车辆安全及运营秩序,目前动车组通过轴温监控轴承故障,但因温度监控存在一定的滞后性等缺点,因此,研发基于振动信号的车轴轴承故障诊断系统具有重要的工程价值。根据轴承实际参数,笔者进行了轴承故障频率计算;为保证理论公式的正确性,笔者按照轴承实际受力方式搭建了轴承振动实验台,对无损伤轴承和局部损伤轴承进行了振动对比测试。同时,测试表明轴承内圈及滚动体故障位置的实时变化会引起故障特征频率极易出现调制,频谱比较杂乱,无法采用常规数据分析方法找到故障频率;另外,转向架轴承故障前期振动信号比较微弱,在高速运行过程中,被来自轮轨激励的强振动信号所掩盖,因此笔者开发了"强信号中提取弱信号"的算法,通过新算法的开发,使得复杂调制现象中的损伤故障特征能够被有效提取出来,可以实现轴承故障的有效识别,基于此开发了轴承故障监测诊断系统,可实现故障早期预警判断等功能。
Bearing damage could cause abnormal vibration and hot shaft of the vehicle,which seriously affected vehicle safety and operational order. At present,the bearing fault should be detected via the shaft temperature monitors which had been widely applied in EMU. However,there still excited some shortcomings such as the time delay of the temperature monitoring. Therefore,the failure diagnose system for axle based on the vibration signal was developed. And which has the great importance in engineering. According to the actual parameters of the bearing,and in order to ensure the correctness of the theoretical formula,the author built the bearing vibration test rig as the basis of the actual bearing load conditions,and carried out the vibration comparison test on the non-damaged bearing and the partially damaged bearing. At the same time,the test showed that the real-time change of the bearing inner ring and the rolling element fault location,which would cause the fault characteristic frequency to be easily modulated. Still,the spectrum was rather messy,and the fault frequency could not be found by the conventional data analysis method. In addition,the vibration signal of the bogie bearing fault was weak where the early fault occurred,also,it was covered by the strong vibration signal from the wheel-rail excitation during the high-speed operation process. Therefore,the author developed the algorithm concepted on "extracting the weak signal from the strong signal". The damage fault feature in the complex modulation phenomenon can be effectively extracted to achieve great identification of bearing faults. As a result,a bearing fault monitoring and diagnosis system has been developed,which can realize early warning and judgment.
Bearing damage could cause abnormal vibration and hot shaft of the vehicle,which seriously affected vehicle safety and operational order. At present,the bearing fault should be detected via the shaft temperature monitors which had been widely applied in EMU. However,there still excited some shortcomings such as the time delay of the temperature monitoring. Therefore,the failure diagnose system for axle based on the vibration signal was developed. And which has the great importance in engineering. According to the actual parameters of the bearing,and in order to ensure the correctness of the theoretical formula,the author built the bearing vibration test rig as the basis of the actual bearing load conditions,and carried out the vibration comparison test on the non-damaged bearing and the partially damaged bearing. At the same time,the test showed that the real-time change of the bearing inner ring and the rolling element fault location,which would cause the fault characteristic frequency to be easily modulated. Still,the spectrum was rather messy,and the fault frequency could not be found by the conventional data analysis method. In addition,the vibration signal of the bogie bearing fault was weak where the early fault occurred,also,it was covered by the strong vibration signal from the wheel-rail excitation during the high-speed operation process. Therefore,the author developed the algorithm concepted on "extracting the weak signal from the strong signal". The damage fault feature in the complex modulation phenomenon can be effectively extracted to achieve great identification of bearing faults. As a result,a bearing fault monitoring and diagnosis system has been developed,which can realize early warning and judgment.
引文
[1]张建军,王仲生,芦玉华,等.基于非线性动力学的滚动轴承故障工程建模与分析[J].振动与冲击,2010,29(11):30-34.
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[4] WANG WENYI. Early detection of gear tooth cracking using the resonance demodulation technique[J]. Mechanical Systems and Signal Processing,2001,15(5):887-903.
[5]高立新,王大鹏,刘保华,等.轴承故障诊断中共振解调技术的应用研究[J].北京工业大学学报,2007,33(1):1-5.
[6] MCFADDEN P D,SMITH J D. Vibration Monitoring of Rolling Element Bearing by the High-Frequency Resonance Technique-a Review[J]. Int. J. Tribology,1984(17):3-10.
[7]周凤星,程耕国,梁巍.共振解调和小波分析在机械故障诊断中的应用[J].系统工程与电子技术,2005,27(6):1128-1131.
[8]夏均忠,刘远宏,冷永刚.微弱信号监测方法的现状分析[J].噪声与振动控制,2011(3):156-161.
[9]王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术[J].航空动力学报,2005,20(4):606-612.
[10]邓四二,王勇,王恒迪.基于IHT的共振解调技术的滚动轴承故障诊断方法[J].航空动力学报,2012,27(1):69-74.
[2]聂晔.动车组转向架轴承可靠性分析与故障诊断的技术研究[D].长沙:中南大学,2011.
[3]东亚斌,廖明夫,高琦.滚动体具有局部缺陷滚动轴承的动力学分析[J].重型机械,2012(3):148-152.
[4] WANG WENYI. Early detection of gear tooth cracking using the resonance demodulation technique[J]. Mechanical Systems and Signal Processing,2001,15(5):887-903.
[5]高立新,王大鹏,刘保华,等.轴承故障诊断中共振解调技术的应用研究[J].北京工业大学学报,2007,33(1):1-5.
[6] MCFADDEN P D,SMITH J D. Vibration Monitoring of Rolling Element Bearing by the High-Frequency Resonance Technique-a Review[J]. Int. J. Tribology,1984(17):3-10.
[7]周凤星,程耕国,梁巍.共振解调和小波分析在机械故障诊断中的应用[J].系统工程与电子技术,2005,27(6):1128-1131.
[8]夏均忠,刘远宏,冷永刚.微弱信号监测方法的现状分析[J].噪声与振动控制,2011(3):156-161.
[9]王平,廖明夫.滚动轴承故障诊断的自适应共振解调技术[J].航空动力学报,2005,20(4):606-612.
[10]邓四二,王勇,王恒迪.基于IHT的共振解调技术的滚动轴承故障诊断方法[J].航空动力学报,2012,27(1):69-74.