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无转速计包络阶次分析及其在变转速滚动轴承故障检测中的应用。

Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds.

机构信息

School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

出版信息

Sensors (Basel). 2013 Aug 16;13(8):10856-75. doi: 10.3390/s130810856.

Abstract

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of rolling element bearings. Conventional diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speed. This constraint limits the bearing diagnosis to the industrial application significantly. In order to extend the conventional diagnostic methods to speed variation cases, a tacholess envelope order analysis technique is proposed in this paper. In the proposed technique, a tacholess order tracking (TLOT) method is first introduced to extract the tachometer information from the vibration signal itself. On this basis, an envelope order spectrum (EOS) is utilized to recover the bearing characteristic frequencies in the order domain. By combining the advantages of TLOT and EOS, the proposed technique is capable of detecting bearing faults under varying speeds, even without the use of a tachometer. The effectiveness of the proposed method is demonstrated by both simulated signals and real vibration signals collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Analyzed results show that the proposed method could identify different bearing faults effectively and accurately under speed varying conditions.

摘要

振动分析是滚动轴承状态监测和故障诊断的有效工具。传统的诊断方法基于静止假设,因此不适用于诊断在变速下工作的轴承。这一限制极大地限制了轴承诊断在工业应用中的应用。为了将传统的诊断方法扩展到速度变化的情况,本文提出了一种无转速包络阶次分析技术。在提出的技术中,首先引入了一种无转速阶次跟踪(TLOT)方法,从振动信号本身中提取转速信息。在此基础上,利用包络阶次谱(EOS)在阶次域中恢复轴承特征频率。通过结合 TLOT 和 EOS 的优点,该技术能够在没有转速计的情况下检测变速下的轴承故障。通过模拟信号和从带有内圈、外圈和滚子故障的机车滚动轴承采集的实际振动信号验证了该方法的有效性。分析结果表明,该方法能够在变速条件下有效地、准确地识别不同的轴承故障。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fdbd/3812632/317da74f0132/sensors-13-10856f1.jpg

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