Fault detection systems are typically applied in the railway industry to examine the structural health status of the wheel/rail system. We herein propose a time-domain kurtosis beamforming technique using an array of microphones for the fault identification and localisation of the wheel/rail system under an environment with high background noise. As an acoustics-based noncontact diagnosis method, this technique overcomes the challenge of the contact between the sensors and examined structures, and it is more applicable for impulsive signals of broadband nature, such as impact noise generated from faults on the wheel surface. Moreover, the application of kurtosis enables the identification and localisation at low signal-to-noise ratio. Under such circumstance, the impulsive signals generated by faults were totally merged in rolling noise and background noise. Meanwhile, different types of faults on the wheels could be identified and localised by observing the kurtosis value on the beamforming sound map. The effectiveness of the proposed method to diagnose the type of wheel fault with low signal-to-noise ratio and moving source has been validated experimentally. This method may provide a useful tool for the routine maintenance of trains.

中文翻译：

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基于峰度波束形成的轮轨系统车轮故障检测

故障检测系统通常应用于铁路行业，以检查轮轨系统的结构健康状况。我们在此提出了一种使用麦克风阵列的时域峰态波束成形技术，用于在具有高背景噪声的环境下对轮轨系统进行故障识别和定位。作为一种基于声学的非接触式诊断方法，该技术克服了传感器与被测结构之间接触的挑战，更适用于宽带性质的脉冲信号，例如车轮表面故障产生的冲击噪声。此外，峰度的应用能够在低信噪比下进行识别和定位。在这样的情况下，故障产生的脉冲信号完全融合在滚动噪声和背景噪声中。同时，通过观察波束成形声图上的峰度值，可以识别和定位车轮上的不同类型的故障。实验验证了所提出的方法在诊断低信噪比和动源车轮故障类型方面的有效性。这种方法可以为列车的日常维护提供有用的工具。