Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform
Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform
复制标题
使用广义同步挤压变换进行齿轮箱故障诊断的时频信号分析
DOI：
10.1016/j.ymssp.2011.07.001
复制
发表时间：
2012-01-01
影响因子：
8.4
通讯作者：
Liang, Ming
中科院分区：
文献类型：
--
作者：
Li, Chuan;Liang, Ming
振动数据，特别是在系统启动和停机期间收集的振动数据，包含了用于齿轮箱状态监测的丰富信息。时频分析是变速箱故障检测的有效工具。然而，调幅-调频（AM-FM）齿轮箱故障信号的特征通常不能直接从时变频率和噪声多分量测量造成的模糊的时频表示（TFR）中提取。因此，我们建议使用广义同步压缩变换（GST）为基础的TF方法来检测和诊断齿轮箱故障。该方法首先将原始振动信号映射到另一个解析信号，以便于TF图像的同步压缩。然后应用时间尺度域恢复过程来恢复具有集中TFR的瞬时频率分布。然后可以通过观察TFR中的啮合频率和边带分量的存在来检测齿轮箱故障（如果有的话）。通过对AM-FM分量的频率相关性分析，可以识别出故障齿轮。所提出的方法进行评估，使用模拟和实验齿轮箱振动信号。结果表明，该方法是有效的齿轮箱状态监测。(C)2011爱思唯尔有限公司版权所有。
The vibration data, especially those collected during the system run-up and run-down periods, contain rich information for gearbox condition monitoring. Time-frequency (TF) signal analysis is an effective tool to detect gearbox faults under varying shaft speed. However, the feature of the amplitude modulated-frequency modulated (AM-FM) gearbox fault signal usually cannot be directly extracted from the blurred time-frequency representation (TFR) caused by the time-varying frequency and noisy multicomponent measurement. As such, we propose to use a generalized synchrosqueezing transform (GST)-based TF method to detect and diagnose gearbox faults. With this method, the original vibration signal is first mapped into another analytical signal to facilitate synchrosqueezing of the TF picture. A time-scale domain restoration process is then applied to recover the instantaneous frequency profile with concentrated TFR The gearbox fault, if any, can then be detected by observing the presence of the meshing frequency and sideband components in the TFR. The faulty gear can be identified via frequency relation analysis of AM-FM components. The proposed method is evaluated using both simulated and experimental gearbox vibration signals. The results show that the proposed approach is effective for gearbox condition monitoring. (C) 2011 Elsevier Ltd. All rights reserved.