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首页> 外文期刊>Mechanical systems and signal processing >Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools
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Transient tracking of low and high-order eccentricity-related components in induction motors via TFD tools

机译：通过TFD工具瞬态跟踪感应电动机中低阶和高阶偏心率相关的组件

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The present work is focused on the diagnosis of mixed eccentricity faults in induction motors via the study of currents demanded by the machine. Unlike traditional methods, based on the analysis of stationary currents (Motor Current Signature Analysis (MCSA)), this work provides new findings regarding the diagnosis approach proposed by the authors in recent years, which is mainly focused on the fault diagnosis based on the analysis of transient quantities, such as startup or plug stopping currents (Transient Motor Current Signature Analysis (TMCSA)), using suitable time-frequency decomposition (TFD) tools. The main novelty of this work is to prove the usefulness of tracking the transient evolution of high-order eccentricity-related harmonics in order to diagnose the condition of the machine, complementing the information obtained with the low-order components, whose transient evolution was well characterised in previous works. Tracking of high-order eccentricity-related harmonics during the transient, through their associated patterns in the time-frequency plane, may significantly increase the reliability of the diagnosis, since the set of fault-related patterns arising after application of the corresponding TFD tool is very unlikely to be caused by other faults or phenomena. Although there are different TFD tools which could be suitable for the transient extraction of these harmonics, this paper makes use of a Wigner-Ville distribution (WVD)-based algorithm in order to carry out the time-frequency decomposition of the startup current signal, since this is a tool showing an excellent trade-off between frequency resolution at both high and low frequencies. Several simulation results obtained with a finite element-based model and experimental results show the validity of this fault diagnosis approach under several faulty and operating conditions. Also, additional signals corresponding to the coexistence of the eccentricity and other non-fault related phenomena making difficult the diagnosis (fluctuating load torque) are included in the paper. Finally, a comparison with an alternative TFD tool -the discrete wavelet transform (DWT) - applied in previous papers, is also carried out in the contribution. The results are promising regarding the usefulness of the methodology for the reliable diagnosis of eccentricities and for their discrimination against other phenomena.
机译：目前的工作集中在通过研究电机所需的电流来诊断感应电动机中的混合偏心故障。与传统方法不同，这项工作基于静态电流分析（Motor Current Signature Analysis（MCSA）），为作者近年来提出的诊断方法提供了新的发现，该方法主要侧重于基于分析的故障诊断。使用合适的时频分解（TFD）工具来确定瞬时量，例如启动或插头停止电流（瞬时电机电流签名分析（TMCSA））。这项工作的主要新颖之处在于证明了跟踪高阶偏心相关谐波的瞬态演化以诊断机器状态的有用性，并补充了瞬态演化良好的低阶分量所获得的信息。在以前的作品中具有特色。通过在时频平面中通过其相关模式跟踪瞬态中与高阶偏心相关的谐波，可以显着提高诊断的可靠性，因为在应用相应的TFD工具后出现的一组与故障相关的模式是极不可能是由其他故障或现象引起的。尽管有不同的TFD工具可能适合于这些谐波的瞬态提取，但本文还是使用基于Wigner-Ville分布（WVD）的算法来对启动电流信号进行时频分解，因为这是一种在高频和低频频率分辨率之间显示出极佳权衡的工具。使用基于有限元的模型获得的一些仿真结果和实验结果表明，该故障诊断方法在几种故障和运行条件下都是有效的。另外，在论文中还包括与偏心率和其他与故障无关的现象并存的附加信号，这些现象使诊断变得困难（负载转矩波动）。最后，在本文的贡献中，还进行了与替代TFD工具（离散小波变换（DWT））的比较。关于该方法对偏心率的可靠诊断及其对其他现象的辨别的有用性，结果令人鼓舞。

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