Autonomous Bearing Tone Tracking Algorithm

Alon Sol, E. Madar, J. Bortman, R. Klein
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Abstract

To date, much of the research done in the field of condition monitoring of rotating machinery is conducted in the frequency domain. The frequency domain analysis specifically for bearings is based on extracting features from the spectrum of the vibration signature. These features are mostly based on the amplitude at the bearing tones along with their sidebands and high order harmonics. Therefore, it is important to determine the location of the mentioned bearing tones in the spectrum accurately and automatically. For the case of ball bearings this process can be problematic due to slippage of the rolling elements and variations in the angle of contact. These may cause the bearing tone to deviate from its nominal value. To this day, the common practice for bearing diagnostics is based on the vibration level at the analytical bearing tones or involvement of experts to identify the true location of the bearing tone. In this research an autonomous algorithm for bearing tone extraction, based on pattern matching, was developed. The proposed algorithm is based on the common assumption that the spectrum of a faulted bearing contains a certain known pattern of prominent peaks. The algorithm “scans” the entire spectrum and determines the frequency value which has the highest correlation to the mentioned pattern. The proposed algorithm was validated and its capabilities are illustrated using experimental data. This algorithm is able to assist any diagnostic approach towards automatic and reliable feature extraction process, both for physics based and data driven approaches.
自主轴承音调跟踪算法
迄今为止,在旋转机械状态监测领域所做的许多研究都是在频域进行的。专门针对轴承的频域分析是基于从振动特征的频谱中提取特征。这些特征主要基于承载音及其边带和高次谐波处的振幅。因此,准确、自动地确定上述轴承音在频谱中的位置是很重要的。对于滚珠轴承的情况下,由于滚动元件的滑动和接触角的变化,这个过程可能会有问题。这些可能导致轴承音调偏离其标称值。直到今天,轴承诊断的常见做法是基于分析轴承音调的振动水平或专家的参与来确定轴承音调的真实位置。本文提出了一种基于模式匹配的自主轴承音调提取算法。提出的算法是基于一个共同的假设,即故障轴承的频谱包含一定的已知模式的突出峰。该算法“扫描”整个频谱,并确定与上述模式相关性最高的频率值。实验数据验证了该算法的有效性。该算法能够帮助任何诊断方法实现自动可靠的特征提取过程,无论是基于物理的还是数据驱动的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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