Sun Qi, Zhu Juan, Chen Chunjun
Institute of Applied Electronics, China Academy of Engineering Physics, Mianyang 621900, China.
PLA Military Space Force, Mianyang 621900, China.
Sensors (Basel). 2024 Nov 30;24(23):7675. doi: 10.3390/s24237675.
Railway traction motor bearings (RTMB) are critical components in high-speed trains (HST) that are particularly susceptible to failure due to the high stress and rotational frequency they experience. To address the challenge of high false-positive rates in existing monitoring systems, this paper introduces a novel sensorless monitoring scheme that leverages stator current to detect fault-related characteristics, eliminating the need for additional sensors. This approach employs a hybrid signal preprocessing algorithm that integrates adaptive notch filtering (ANF) with envelope spectrum analysis (ESA) to effectively sparse the stator current and extract relevant fault features. A deep belief network (DBN) is utilized for the classification of the health status of the RTMB. To validate the scheme's feasibility and effectiveness, we conducted experiments on a 1:1 scale high-speed railway traction motor, demonstrating that mechanical defects in RTMB can be reliably indicated by changes in stator current. Based on the analysis of experimental results, it was concluded that the fault detection accuracy of RTMB based on stator current is at least 17.3% higher than that of the fault diagnosis methods based on vibration in diagnosing whether the system has a fault. Among them, the method proposed in this paper is the best in diagnosing the presence and type of faults, with an accuracy that is at least 8.9% higher than other methods. This study not only presents a new method for RTMB monitoring but also contributes to the field by offering a more accurate and efficient alternative to current practices.
铁路牵引电机轴承（RTMB）是高速列车（HST）中的关键部件，由于其所承受的高应力和高旋转频率，特别容易发生故障。为应对现有监测系统中误报率高的挑战，本文介绍了一种新颖的无传感器监测方案，该方案利用定子电流来检测与故障相关的特征，无需额外的传感器。这种方法采用了一种混合信号预处理算法，该算法将自适应陷波滤波（ANF）与包络谱分析（ESA）相结合，以有效地稀疏定子电流并提取相关的故障特征。利用深度信念网络（DBN）对RTMB的健康状态进行分类。为验证该方案的可行性和有效性，我们在1:1比例的高速铁路牵引电机上进行了实验，结果表明定子电流的变化能够可靠地指示RTMB中的机械缺陷。基于实验结果分析得出，在诊断系统是否存在故障时，基于定子电流的RTMB故障检测准确率比基于振动的故障诊断方法至少高17.3%。其中，本文提出的方法在诊断故障的存在和类型方面表现最佳，其准确率比其他方法至少高8.9%。本研究不仅提出了一种用于RTMB监测的新方法，还通过提供一种比当前实践更准确、高效的替代方法，为该领域做出了贡献。
