Huang Huan, Baddour Natalie, Liang Ming
Department of Mechanical Engineering, University of Ottawa, Ottawa, Ontario, Canada.
MethodsX. 2019 Jun 11;6:1415-1432. doi: 10.1016/j.mex.2019.05.020. eCollection 2019.
Vibration 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. •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.
振动信号分析是轴承故障诊断的一项重要技术。对于在恒定转速下运行的轴承，由于每种故障都有特定的故障特征频率（FCF），且该频率与轴转速成正比，因此可以在频域中诊断故障。然而，轴承通常在变速条件下运行。此外，测量变速转速需要诸如转速计等仪器，这会导致额外的成本和安装工作。随着时频分析的发展，时变FCF在时频表示（TFR）中表现为曲线。研究表明，从TFR中提取多条时频曲线，然后识别瞬时故障特征频率（IFCF）和瞬时轴转速频率（ISRF），无需使用转速计即可在变速条件下自动诊断轴承故障。然而，现有的用于识别IFCF和ISRF的方法可能会导致结果不准确。在本研究中，给出了完整的MATLAB代码以及一种更可靠的方法，用于在变速条件下使用多时间频率曲线提取（MTFCE）进行轴承故障自动诊断。•给出了一个多时间频率曲线提取（MTFCE）的MATLAB代码，用于从TFR中提取多条曲线。•给出并解释了通过MTFCE在不使用转速计数据的情况下进行变速条件下轴承故障自动诊断的自定义MATLAB代码。•提出了一个新参数——曲线间比率的允许方差，以更可靠地识别IFCF和ISRF。
