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Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features

机译：基于时域特征自相关的低速轴承故障诊断自动化

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摘要

This study is focused on the application of automated techniques on low-speed bearing diagnostics. The diagnosis in low-speed conditions is hampered by the long periods between defect-related impulses and the high level of noise relative to the magnitude of the impulses. To detect a localised defect in such conditions, a new approach that uses vibration signals and information on the bearing defect frequencies is proposed. At first, the vibration signal is filtered in a specific frequency range to enable the detection of the impulses hidden in the signal. The filtered signal is then segmented into short time windows, the length of which are selected based on the bearing defect frequencies. Statistical time domain features are calculated from these windows to amplify and compress the impulses inflicted by the defect. Then, a criterion based on the autocorrelation values of specific time lags is calculated. An exhaustive search procedure is used to determine the frequency band for signal filtering and to select the statistical feature, which together maximises the proposed criterion. The highest value of the criterion is finally compared with the corresponding value from the baseline condition to detect the localised defect. The proposed technique is demonstrated on simulated signals, and validated based on the vibration signals from laboratory tests with undamaged, slightly damaged and severely damaged rolling elements in a rolling element bearing. Different conditions with shaft speeds from 20 to 80 rpm were studied in the laboratory tests. The proposed technique was compared with automated envelope spectrum diagnosis approaches based on the peak ratio and peak-to-median indicators and the fast kurtogram. The results reveal that the criterion based on autocorrelation gave defect indications associated with the correct type of defect in various circumstances while the tested envelope spectrum approaches were prone to induce an incorrect conclusion. Moreover, the results indicate that the approach could be used successfully on signals with a length that includes relatively few defect periods or impulses. The approach requires a high sampling rate relative to the defect frequencies, which may limit its suitability for the higher shaft speeds.
机译：这项研究的重点是自动化技术在低速轴承诊断中的应用。在低速条件下的诊断受到与缺陷相关的脉冲之间的长时间间隔以及相对于脉冲大小的高噪声水平的困扰。为了在这种情况下检测局部缺陷，提出了一种使用振动信号和轴承缺陷频率信息的新方法。首先，在特定的频率范围内对振动信号进行滤波，以检测隐藏在信号中的脉冲。然后，将滤波后的信号分段为多个短时间窗口，并根据轴承缺陷频率选择其长度。从这些窗口中计算出统计时域特征，以放大和压缩缺陷造成的脉冲。然后，基于特定时滞的自相关值的准则被计算。详尽的搜索过程用于确定信号滤波的频带并选择统计特征，这一起使所提出的标准最大化。最后，将标准的最大值与基线条件下的相应值进行比较，以检测局部缺陷。所提出的技术在模拟信号上进行了演示，并根据来自实验室测试的振动信号进行了验证，该振动信号是滚动轴承中未损坏，轻微损坏和严重损坏的滚动元件。在实验室测试中研究了轴转速从20到80 rpm的不同条件。将该技术与基于峰比率和峰均值指标以及快速峰图的自动包络光谱诊断方法进行了比较。结果表明，基于自相关的标准给出了在各种情况下与正确类型的缺陷相关的缺陷指示，而测试的包络谱方法则容易得出错误的结论。而且，结果表明该方法可以成功地用于长度包括相对较少的缺陷周期或脉冲的信号。该方法需要相对于缺陷频率的高采样率，这可能会限制其对更高轴速的适用性。

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