基于MCKD的轧机齿轮故障源辨识与安全性评估

Fault Source Identification and Safety Evaluation of Gearbox in Rolling Mill Based on MCKD

轧机齿轮箱是轧钢生产线的关键设备,生产线的运行安全很大程度上取决于齿轮传动系统的运行状态。由于轧钢设备的结构复杂,导致系统内部振源数量众多,齿轮振动信号会受到很强的噪声干扰。在齿轮箱的故障诊断研究中,如何滤除观测信号中的噪声和干扰成分,尽最大可能恢复故障源的特征信息,历来都是研究的热点和难点问题。本文引入最大相关峭度反卷积算法(MCKD)算法,以高线轧机齿轮箱为研究对象,力图解决强噪声环境下故障源信息的分离与提取问题。分析结果表明,故障信号经MCKD处理后,故障源的冲击特征得以增强,为齿轮箱的安全性评估提供了重要依据。

Gearbox is one of the most important equipments in the rolling mills. As a result, the safety of whole production line will rely on the operation status of the transmission gearbox. Due to complicated structure of rolling equipments, the vibration sources in the system are so many that the gear - induced vibration will be over- whelmed by strong noise. In the fault diagnosis direction, how to suppress the disturbance and recover the feature in- formation related to the fault source is always the research focus. Based on Maximum Correlated Kurtosis Deconvolu- tion （MCKD） algorithm, the gearbox in the high speed wire mills is selected as the research object to investigate the fault source identification from strong background noise. The results indicate that the shock feature will be enhanced by MCKD, which will provide important basis for safety evaluation of gearbox.