Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China.
Sensors (Basel). 2013 Nov 18;13(11):15726-46. doi: 10.3390/s131115726.
The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.
机车轴承作为列车的重要组成部分，其状态对于列车安全至关重要。在高速运动中，多普勒效应会显著扭曲声信号，使得机车轴承的在线监测变得极具挑战性。本研究提出了一种新的基于声学理论和拉普拉斯小波的多普勒瞬态模型，用于识别声信号中嵌入的与故障相关的冲击间隔。在频域中，通过对瞬态模型和真实故障信号的包络谱相关评估来优化模型参数。该方法可以从声信号中识别模拟瞬态（模拟瞬态中的周期）所使用的参数。因此，可以基于识别的参数（特别是周期间隔）成功检测局部轴承故障。该方法在受多普勒效应影响的模拟信号上进行了性能测试。此外，还分别使用该方法对带有内圈和外圈故障的机车轴承的真实声信号进行了分析。结果证实，能够成功检测代表机车轴承故障特征的瞬态之间的周期。
