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A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection

机译：倒谱编辑方法作为基于振动的轴承故障检测的信号预处理技术的比较

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摘要

The detection and diagnosis of incipient rolling element bearing faults is not an undemanding task and signal analysis of vibration measurements therefore often incorporates the use of various complex processing techniques. One of the key steps in the processing procedure is the proper separation of the bearing signal from other influencing sources like shafts or gears. The latter sources produce deterministic signal components showing up as discrete frequencies in the amplitude spectrum, while bearing signals are assumed to be (quasi-) cyclostationary resulting in a smearing of the bearing frequencies in the spectrum. This property gave rise to the idea of using the cepstrum for the purpose of separating the deterministic signal content from the second-order cyclostationary bearing signal. The cepstrum essentially groups the deterministic multi-harmonic signal content in a cepstral peak at the corresponding quefrency, making it more suitable for easy removal of the discrete frequency peaks. Even though initially there was a tendency to only remove or 'lifter' the selected cepstral peaks, nowadays the full real cepstrum is set to zero instead of only certain quefrency bands. This technique, called cepstrum pre-whitening, is easy to implement, can be performed quickly without the need for additional input parameters or fine-tuning and would be well-suited for practical applications. However, these advantages do come at the cost of some control and insight over the editing procedure of the signal. In order to assess the performance of this cepstrum pre-whitening technique, it is compared to an automated cepstrum editing procedure. It automatically selects certain peaks in the real cepstrum and only sets them to zero instead of the full real cepstrum. Both methods perform quite well in separating deterministic signal content from more random content, but there are some differences to observe when using them for diagnosis purposes. An analysis of the methods is made for both simulated and experimental signals.
机译：滚动轴承初始故障的检测和诊断并不是一项艰巨的任务，因此，振动测量的信号分析通常会结合使用各种复杂的处理技术。处理过程中的关键步骤之一是将轴承信号与其他影响源（如轴或齿轮）正确分离。后者的信号源产生确定性的信号分量，这些信号分量在振幅谱中显示为离散频率，而轴承信号被假定为（准）循环平稳的，从而导致频谱中的轴承频率出现拖尾现象。该性质引起了使用倒谱的想法，以将确定性信号内容与二阶循环平稳方位信号分离。倒频谱实质上将确定性多谐波信号含量分组在对应频率处的倒频谱峰中，从而使其更适合于轻松去除离散频率峰值。即使最初倾向于仅去除或“提升”选定的倒谱峰，但如今，整个真实倒谱都设置为零，而不是仅设置某些频率谱带。这项称为倒频谱预增白的技术易于实施，可以快速执行，而无需其他输入参数或微调，非常适合于实际应用。但是，这些优势的确是以牺牲对信号编辑过程的控制和洞察力为代价的。为了评估此倒频谱预增白技术的性能，将其与自动倒频谱编辑程序进行了比较。它会自动选择真实倒谱中的某些峰，并仅将它们设置为零而不是完整的真实倒谱。两种方法在将确定性信号内容与更多随机内容分开方面都表现出色，但是在将其用于诊断目的时要观察一些差异。分析了模拟和实验信号的方法。

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