class='kwd-title'>Method name: Automatic bearing'/> Multiple time-frequency curve extraction Matlab code and its application to automatic bearing fault diagnosis under time-varying speed conditions
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Multiple time-frequency curve extraction Matlab code and its application to automatic bearing fault diagnosis under time-varying speed conditions

机译:多时频曲线提取Matlab代码及其在时变转速条件下轴承自动故障诊断中的应用

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摘要

class="kwd-title">Method name: Automatic bearing fault diagnosis under time-varying speed conditions via multiple time-frequency curve extraction class="kwd-title">Keywords: Multiple time-frequency curve extraction, Time-frequency representations, Instantaneous frequency, Bearing fault diagnosis, Time-varying speed class="head no_bottom_margin" id="abs0010title">AbstractVibration signal analysis is an important technique for bearing fault diagnosis. For bearings operating under constant rotational speed, faults can be diagnosed in the frequency domain since each type of fault has a specific Fault Characteristic Frequency (FCF), which is proportional to the shaft rotational speed. However, bearings often operate under time-varying speed conditions. Additionally, the measurement of the time-varying rotational speed requires instruments, such as tachometers, which leads to extra cost and installation. With the development of time-frequency analysis, the time-varying FCFs manifest as curves in the Time-Frequency Representation (TFR). It has been shown that extracting multiple time-frequency curves from the TFR and then identifying the Instantaneous Fault Characteristic Frequency (IFCF) and Instantaneous Shaft Rotational Frequency (ISRF), bearing faults can be automatically diagnosed under time-varying speed conditions without using tachometers. However, the existing method used to identify the IFCF and the ISRF may lead to inaccurate results. In this study, the complete MATLAB© codes and a more reliable approach to use Multiple Time-Frequency Curve Extraction (MTFCE) for automatic bearing fault diagnosis under time-varying speed conditions are presented. class="first-line-outdent" id="lis0005">
  • • A Multiple time-frequency curve extraction (MTFCE) Matlab code is presented to extract multiple curves from the TFR.
  • • Custom Matlab code for automatic bearing fault diagnosis under time-varying speed conditions without using tachometer data via the MTFCE is given and explained.
  • • A new parameter, the allowable variance of the curve-to-curve ratio, is proposed to identify the IFCF and ISRF more reliably.
  • 机译:<!-fig ft0-> <!-fig @ position =“ anchor” mode =文章f4-> <!-fig mode =“ anchred” f5-> <!-fig / graphic | fig / alternatives / graphic mode =“ anchored” m1-> class =“ kwd-title”>方法名称:在时变速度条件下通过多次时频曲线提取自动进行轴承故障诊断 class =“ kwd-title”>关键字:多个时频曲线提取,时频表示,瞬时频率,轴承故障诊断,时变速度 class =“ head no_bottom_margin” id =“ abs0010title”>摘要振动信号分析是轴承故障诊断的重要技术。对于以恒定转速运行的轴承,可以在频域中诊断故障,因为每种类型的故障都有特定的故障特征频率(FCF),该频率与轴转速成正比。但是,轴承通常在时变速度条件下运行。另外,时变转速的测量需要诸如转速计之类的仪器,这导致额外的成本和安装。随着时频分析的发展,时变FCF在时频表示(TFR)中表现为曲线。已经显示,从TFR中提取多个时频曲线,然后识别瞬时故障特征频率(IFCF)和瞬时轴旋转频率(ISRF),可以在时变速度条件下自动诊断轴承故障,而无需使用转速表。但是,用于识别IFCF和ISRF的现有方法可能会导致结果不准确。在这项研究中,提出了完整的MATLAB©代码以及在时变速度条件下使用多时频曲线提取(MTFCE)进行轴承故障自动诊断的更可靠方法。 class =“ first-line-outdent” id =“ lis0005”> <!-list-behavior =简单的前缀-word = mark-type = none max-label-size = 9->
  • •多时频曲线提取(MTFCE)给出了Matlab代码以从TFR中提取多条曲线。
  • •用于在时变速度条件下自动诊断轴承故障的自定义Matlab代码,无需通过MTFCE使用转速计数据
  • •提出了一个新参数,即曲线与曲线比率的允许方差,以更可靠地识别IFCF和ISRF。
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