Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery
Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery
复制标题
使用改进的快速空间频谱系综峰度峰图检测瞬态振动特征及其在旋转机械短持续时间数据的机械特征分析中的应用
DOI：
10.1016/j.ymssp.2013.03.021
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发表时间：
2013-10-01
影响因子：
8.4
通讯作者：
Sun, Chuang
中科院分区：
文献类型：
--
作者：
Chen, BinQiang;Zhang, ZhouSuo;Sun, Chuang
瞬时振动特征的检测对于旋转机械的振动状态监测和故障检测具有重要意义。然而，振动传感器采集的原始机械信号通常是安装在被检查机械中的多个机械部件的物理振动的混合。被干扰内容掩盖的故障产生的早期振动特征很难识别。FAST Kurtogram(FK)是一种简明而智能的小工具，用于表征这些振动特征。当存在强干扰振动时，调频信号的多速率滤波器组(MRFB)和频谱峰度(SK)指标的能力较弱，特别是当调频信号被应用于短持续时间的振动信号时。由于机械故障引起的脉冲干扰内容不真实，使最优分析过程复杂化，并导致最优分析子带的错误选择，因此原始FM可能会遗漏本质故障特征。为了提高调频信号在工业应用中的分析性能，提出了一种改进的快速峰图--“快速空间谱系综峰图”。在该技术中，采用离散准解析小波紧框架(QAWTF)展开方法作为检测滤波器。与传统的小波包变换相比，基于对偶树复小波变换构造的QAWTF具有更好的振动暂态特征提取能力和增强的时频局部化能力。此外，在所构造的QAWTF中，提出了一种非二进集成小波子带生成策略，以产生能够识别位于小波变换过渡带的故障特征的额外小波子带。另一方面，提出了一种增强的信号冲动性评价指标--“空间谱系综峰度”(SSEK)，并将其作为选择最优分析参数的定量指标。SSEK指标对高斯噪声、谐波和零星脉冲冲击具有较好的抑制能力，在评价振动信号的冲击强度方面具有较强的鲁棒性。数值验证、实验测试和两个工程应用验证了该方法的有效性。数值验证、实验测试和工程应用的分析结果表明，与原有的FM方法和基于小波变换的FK方法相比，该方法具有较强的鲁棒性暂态振动含量检测性能，特别是在处理相对有限持续时间的振动信号时。(C)2013爱思唯尔有限公司。保留所有权利。
Detecting transient vibration signatures is of vital importance for vibration-based condition monitoring and fault detection of the rotating machinery. However, raw mechanical signals collected by vibration sensors are generally mixtures of physical vibrations of the multiple mechanical components installed in the examined machinery. Fault-generated incipient vibration signatures masked by interfering contents are difficult to be identified. The fast kurtogram (FK) is a concise and smart gadget for characterizing these vibration features. The multi-rate filter-bank (MRFB) and the spectral kurtosis (SK) indicator of the FM are less powerful when strong interfering vibration contents exist, especially when the FM are applied to vibration signals of short duration. It is encountered that the impulsive interfering contents not authentically induced by mechanical faults complicate the optimal analyzing process and lead to incorrect choosing of the optimal analysis subband, therefore the original FM may leave out the essential fault signatures. To enhance the analyzing performance of FM for industrial applications, an improved version of fast kurtogram, named as "fast spatial-spectral ensemble kurtosis kurtogram", is presented. In the proposed technique, discrete quasi-analytic wavelet tight frame (QAWTF) expansion methods are incorporated as the detection filters. The QAWTF, constructed based on dual tree complex wavelet transform, possesses better vibration transient signature extracting ability and enhanced time-frequency localizability compared with conventional wavelet packet transforms (WPTs). Moreover, in the constructed QAWTF, a non-dyadic ensemble wavelet subband generating strategy is put forward to produce extra wavelet subbands that are capable of identifying fault features located in transition-band of WPT. On the other hand, an enhanced signal impulsiveness evaluating indicator, named "spatial-spectral ensemble kurtosis" (SSEK), is put forward and utilized as the quantitative measure to select optimal analyzing parameters. The SSEK indicator is robuster in evaluating the impulsiveness intensity of vibration signals due to its better suppressing ability of Gaussian noise, harmonics and sporadic impulsive shocks. Numerical validations, an experimental test and two engineering applications were used to verify the effectiveness of the proposed technique. The analyzing results of the numerical validations, experimental tests and engineering applications demonstrate that the proposed technique possesses robuster transient vibration content detecting performance in comparison with the original FM and the WPT-based FK method, especially when they are applied to the processing of vibration signals of relative limited duration. (c) 2013 Elsevier Ltd. All rights reserved.