基于小波系数提取及离散余弦包络分析的机车牵引齿轮故障诊断方法

Fault Diagnosis Method of Locomotive Driven Gear Based on Envelopment Analysis of Wavelet Coefficients Extraction and DCT

机车走行部故障是危及列车运行安全的重要因素,其故障程度的实时监测与诊断是高速重载列车需要解决的关键问题之一。本文以机车走行部的牵引齿轮为例,在分析总结大量实际振动信号的基础上,探讨了运用小波分解和离散余弦变换相结合来提取机车牵引齿轮故障特征信息的方法,即首先采用小波基将牵引齿轮故障引起的振动信号变换到时间-尺度域,再对高频段的小波系数采用离散余弦变换进行包络分析;阐述了该方法在机车走行部在线故障诊断系统中的应用,不仅能满足在线状态监测与故障诊断的实时性和可靠性要求,而且具有很强的抗干扰能力。实际应用表明,该方法能可靠提取牵引齿轮的故障特征信息,系统能快速、准确地诊断出故障类型及程度,可有效用于机车走行部故障的在线监测与诊断。

The fault of the locomotive running gear is a grave danger to train safety. The real-time monitor and diagnosis of the high-speed and the heavy-load trains is one of the key points to be solved. Based on the analysis of the vibration signals, this paper has discussed the method of fault characteristic extraction of the locomotive traction gear through the discrete cosine transform（DCT）combined with the wavelet decomposition . Firstly, the wavelet base was used to transform the vibration signals to the time criterion range, and then the envelopment analysis was made to the wavelet coefficients in the high range through DCT. The application to the online fault diagnosis of the locomotive running gear was expounded. The result shows hat this method can satisfy the standard of the real time monitor and the reliability and is of a strong anti-jamming ability. This method is efficient in real-time monitor and diagnosis of the locomotive running gear as it can extract the fault charaeteristics and diagnose them quickly and accurately.