Title: Monitoring the lack of grease condition of rolling bearing using acoustic emission

Authors: Kaiqiang Wang; Xiaoqin Liu; Xing Wu; Zhenjun Zhu

Addresses: Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, 727 South Jingming Road, Kunming, China ' Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, 727 South Jingming Road, Kunming, China ' Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, 727 South Jingming Road, Kunming, China ' Vehicle Manufacturing Engineering Department, SAIC General Motors Corporation Limited, 1500 Shen Jiang Road, Jin Qiao, Pu Dong, Shanghai, China

Abstract: Rolling element bearings are the vital parts of machines, and their condition is often critical to the operation or process. Lubricant such as grease can present a film between the bearing surfaces and minimises the friction and wear. Lack of lubricant may lead to ineffective performance or malfunction of the bearing. Therefore, in order to avoid unexpected breakdowns, reliable lubrication monitoring techniques are demanded. Acoustic emission (AE) technology can detect the friction between moving parts in the machines. The object of this paper is to evaluate the grease amount in the rolling element bearing with AE signals. Four parameters of AE are studied, including event count rate, ring count per event, energy rate, and RMS. The first three parameters are derived from AE parameter analysis, and RMS is calculated directly on the continuously sampled signal. Eight amounts of the grease in the same bearing are tested. Experiments on grease consumption with running time are also carried out. According to the results, RMS and energy rate can be used to estimate the remaining amount of the lubricant. The method is also verified by field tests on articulated industrial robots.

Keywords: acoustic emission; lubrication; rolling bearings; condition monitoring; grease.

DOI: 10.1504/IJMIC.2019.096814

International Journal of Modelling, Identification and Control, 2019 Vol.31 No.1, pp.94 - 102

Available online: 14 Nov 2018 *

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