方位(导航)
曲面(拓扑)
材料科学
结构工程
疲劳试验
法律工程学
工程类
计算机科学
数学
几何学
人工智能
作者
Santhosh Kumar Kamarapu,M. Amarnath,Perumalla Sateesh Kumar,Deepak Kumar Prasad,B. S. Ajay Vardhaman
标识
DOI:10.1177/14644207241254448
摘要
Rolling element bearings are the most important components in almost all rotating machines. These bearings are often subjected to repetitive load cycles at different operating conditions. Excessive loads, speeds, and improper operating conditions lead to the propagation of defects on their load-bearing surfaces, thereby causing a negative impact on the performance of rotating machines. This paper presents the results of experimental investigations to assess wear propagation in roller bearings using lubricant degradation, vibration, and statistical parameter analysis methods. A roller bearing setup was developed in the laboratory, and the test bearing (NJ 307E) was subjected to fatigue tests over a period of 900 h. The bearing was operated at a speed and radial load of 800 rpm and 1 kN, respectively. The film thickness analysis revealed a transition in lubrication regimes during 600–900 h of operation. Grease structure degradation and oxidation analyses were carried out using scanning electron microscope images and the Fourier transform infrared radiation technique. Further, the vibration signals are extracted from the bearing housing at regular intervals. Using the fast Fourier transform technique, these vibration signals were used to analyze bearing fault frequencies to highlight the faults developed on bearing contact surfaces. The statistical features of vibration signals such as root mean square, kurtosis, and crest factor were used to assess the severity of wear propagated on the bearing contact surfaces. Integrating tribological and vibration parameter analysis techniques provided a reliable assessment of surface fatigue wear propagated on the roller-bearing contact surfaces.
科研通智能强力驱动
Strongly Powered by AbleSci AI