Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis
Rolling element bearing fault diagnosis via fault characteristic order (FCO) analysis
复制标题
通过故障特征阶次 (FCO) 分析进行滚动轴承故障诊断
DOI：
10.1016/j.ymssp.2013.11.011
复制
发表时间：
2014-03-03
影响因子：
8.4
通讯作者：
Cheng, Weidong
中科院分区：
文献类型：
--
作者：
Wang, Tianyang;Liang, Ming;Cheng, Weidong
基于时频表示（TFR）的阶次跟踪是在不使用转速表的情况下进行时变转速下齿轮故障检测的最有效方法之一。然而，对于滚动轴承，通常不能直接从TFR中提取与转速相关的信号分量。因此，我们提出了一种新的方法来解决这个问题。该方法包括四个主要步骤：(a)通过快速谱峰度（SK）分析进行信号滤波——与短时傅立叶变换（STFT）一起，得到具有清晰故障显示趋势线的滤波信号的TFR， (b)使用基于幅值和的谱峰搜索算法从TFR中提取瞬时故障特征频率（IFCF）。(c)基于提取的IFCF对信号进行重采样，将非平稳时域信号转换为平稳故障相角（FPA）域信号；(d)将FPA域信号转换为故障特征阶（FCO）域，并从FCO谱中识别故障类型。仿真和实验结果验证了该方法的有效性。(C) 2013 Elsevier Ltd.版权所有。
Order tracking based on time frequency representation (TFR) is one of the most effective methods for gear fault detection under time-varying rotational speed without using a tachometer. However, for a rolling element bearing, the signal components related to rotational speed usually cannot be directly extracted from the TFR. As such, we propose a new method to solve this problem. This method consists of four main steps: (a) signal filtering via fast spectral kurtosis (SK) analysis - this together with the short time Fourier transform (STFT) leads to a TFR of the filtered signal with clear fault-revealing trend lines, (b) extraction of instantaneous fault characteristic frequency (IFCF) from the TFR using an amplitude-sum based spectral peak search algorithm, (c) signal resampling based on the extracted IFCF to convert the non-stationary time-domain signal into the stationary fault phase angle (FPA) domain signal, and (d) transform of the FPA domain signal into the domain of the fault characteristic order (FCO) and identification of fault type from the FCO spectrum. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals. (C) 2013 Elsevier Ltd. All rights reserved.