Year: 2025
Type: article
Abstract: 轴箱轴承作为列车走形部件中的重要部件之一，保证其在运用过程中的安全性，可以通过轴箱的振动信号提取出轴承的故障特征对轴承的健康状态进行监测，然而实际运用过程中，我们很难捕捉到轴承处于故障状态下的轴箱振动信号，且故障轴承不允许参与行车试验。本文将轴承部件引入到车辆–轨道耦合模型之中，构造更为接近真实运用条件下的轴承动力学模型，并基于此模型，构造轴承早期缺陷模型，仿真获得耦合作用下轴箱轴承的故障信号，并对信号进行Hilbert和FFT变换提取出轴承的故障特征。研究结果表明，本文构造的轴承故障信号极其微弱，时域信号分析不易察觉，符合早期故障的特征；故障特征最大幅值所对应的频率出现在以内圈转动频率乘以双列滚子数为中心的频率带内，靠近频率带中心；在列车运行速度发生变化时，信号的包络时–频图依然能够清晰地反映轴承的故障特征，相比于直接对信号进行FFT变换具有明显的优势；角域重采样技术和轴承故障特征因子能够使故障特征便于观察，本文的信号处理分析处理方法能够对轴箱轴承进行实时故障检测。Axle box bearing is one of the important parts of the train running parts to ensure its safety in the operation process. The fault characteristics of the bearing can be extracted from the axle box vibration signal to monitor the health state of the bearing. However, in the actual application process, it is difficult for us to capture the axle box vibration signal when the bearing is in the fault state, and the fault bearing is not allowed to participate in the driving test. In this paper, the bearing components are introduced into the vehicle-track coupling model to construct a bearing dynamic model closer to the real application conditions. Based on this model, the early bearing defect model is constructed, the fault signal of axle box bearing under coupling is simulated, and the fault characteristics of bearing are extracted by Hilbert and FFT transforms. The results show that the bearing fault signal constructed in this paper is extremely weak and difficult to detect in time domain signal analysis, which is in line with the characteristics of early fault. The frequency corresponding to the maximum amplitude of fault characteristics appears in the frequency band centered on the inner ring rotation frequency multiplied by the number of double-row rollers, close to the center of the frequency band. When the train speed changes, the envelope time-frequency diagram of the signal can still clearly reflect the fault characteristics of the bearing, which has obvious advantages over the direct FFT transformation of the signal. Angle domain resampling technology and bearing fault feature factor can make fault features easy to observe. The signal processing method in this paper can detect the real-time fault of axle box bearing.
Author: 开成 周
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410978415