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首页> 外文期刊>Shock and vibration >Alpha-Stable Distribution and Multifractal Detrended Fluctuation Analysis-Based Fault Diagnosis Method Application for Axle Box Bearings
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Alpha-Stable Distribution and Multifractal Detrended Fluctuation Analysis-Based Fault Diagnosis Method Application for Axle Box Bearings

机译：基于α稳定分布和多分形趋势波动分析的轴箱轴承故障诊断方法

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摘要

A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off-line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band-pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha-stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.
机译：铁路车辆的关键部件，例如轮对踏板和轴箱轴承，经常会遭受疲劳破坏。如果不及时发现和处理这些故障，将会严重影响铁路车辆的行驶安全。为了检测这些组件的早期故障并延长其疲劳寿命，定期维护变得至关重要。目前，轴箱轴承的日常维护主要依靠人工离线检查，工作效率低，故障诊断精度高。为了提高铁路车辆的维修效率和有效性，本研究提出了一种将轴箱轴承振动监测系统集成到地板轮对车床中的方法，介绍了该系统的集成方案和工作流程，然后介绍了该方法。详细的故障诊断方法和应用实例。首先，对加速度计采集的原始信号依次进行带通滤波器和包络分析。其次，对包络信号进行了α稳定分布（ASD）分析和多分形去趋势波动分析（MFDFA）分析，提取了五个具有显着稳定性和灵敏度的特征参数，并将其引入基于粒子的最小二乘支持向量机。群优化以定量确定轴承的状态。最后，通过实验数据验证了该方法的有效性。结果表明，与ASD或MFDFA的单一方法相比，所提方法能够有效地诊断轴箱轴承的故障位置和故障程度，并具有较高的诊断精度。

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