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Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions

机译:倒谱预增白在变速条件下轴承故障诊断中的应用

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Diagnostics of rolling element bearings involves a combination of different techniques of signal enhancing and analysis. The most common procedure presents a first step of order tracking and synchronous averaging, able to remove the undesired components, synchronous with the shaft harmonics, from the signal, and a final step of envelope analysis to obtain the squared envelope spectrum. This indicator has been studied thoroughly, and statistically based criteria have been obtained, in order to identify damaged bearings. The statistical thresholds are valid only if all the deterministic components in the signal have been removed. Unfortunately, in various industrial applications, characterized by heterogeneous vibration sources, the first step of synchronous averaging is not sufficient to eliminate completely the deterministic components and an additional step of pre-whitening is needed before the envelope analysis. Different techniques have been proposed in the past with this aim: The most widely spread are linear prediction filters and spectral kurtosis. Recently, a new technique for pre-whitening has been proposed, based on cepstral analysis: the so-called cepstrum pre-whitening. Owing to its low computational requirements and its simplicity, it seems a good candidate to perform the intermediate pre-whitening step in an automatic damage recognition algorithm. In this paper, the effectiveness of the new technique will be tested on the data measured on a full-scale industrial bearing test-rig, able to reproduce the harsh conditions of operation. A benchmark comparison with the traditional pre-whitening techniques will be made, as a final step for the verification of the potentiality of the cepstrum pre-whitening.
机译:滚动轴承的诊断涉及信号增强和分析的不同技术的组合。最常见的过程是阶跃跟踪和同步平均的第一步,能够从信号中去除与轴谐波同步的不需要的分量,以及包络分析的最后一步以获得平方包络谱。对该指示器进行了深入研究,并获得了基于统计的标准,以识别损坏的轴承。统计阈值仅在信号中的所有确定性成分均已删除的情况下才有效。不幸的是,在以异质振动源为特征的各种工业应用中,同步平均的第一步不足以完全消除确定性分量,并且在包络分析之前还需要额外的预白化步骤。过去已针对此目的提出了不同的技术:最广泛使用的是线性预测滤波器和光谱峰度。最近,基于倒频谱分析,提出了一种用于预增白的新技术:所谓的倒频谱预增白。由于其较低的计算要求和简单性,在自动损伤识别算法中执行中间预白化步骤似乎是一个不错的选择。在本文中,将使用在大型工业轴承试验台上测得的数据测试新技术的有效性,该数据能够再现恶劣的运行条件。将进行与传统的预增白技术的基准比较,作为验证倒谱预增白潜力的最后一步。

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