The detection of wheelset bearing faults is of extreme importance for railway vehicle operation safety. Wheelset bearing faults induce impulses in vibration signals, which are hard to detect because of signal modulation and environmental noise. To suppress noise and identify these impulses effectively, we propose a method that leverages morphological signal and image processing techniques. The proposed method mainly includes two aspects: a novel double cross‐correlation operation for noise reduction and an improved image processing algorithm for highlighting the fault features. The performance of the proposed method was tested using real vibration signals collected from a wheelset bearing test rig and compared with other advanced methods reported in the literature. By analysing two wheelset bearing faults, namely, an outer race fault and a pin roller fault, the proposed method is demonstrated to be more effective in the detection of wheelset bearing faults.

中文翻译：

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利用形态学信号和图像分析的轮对轴承故障检测

轮对轴承故障的检测对于铁路车辆的运行安全至关重要。轮对轴承故障会在振动信号中产生脉冲，由于信号调制和环境噪声，这些脉冲很难检测到。为了抑制噪声并有效地识别这些脉冲，我们提出了一种利用形态信号和图像处理技术的方法。所提出的方法主要包括两个方面：一种用于减少噪声的新颖的双互相关运算和一种用于突出显示故障特征的改进的图像处理算法。使用从轮对轴承测试台收集的真实振动信号测试了该方法的性能，并与文献中报道的其他高级方法进行了比较。通过分析两个轮对轴承故障，即