Design of Fatigue Life Testing Machine for Slewing Bearing Based on LabVIEW
- Kang Chen, Pei-Tao Yang, Guo-Xiang Hou
- Corresponding Author
- Kang Chen
Available Online December 2016.
- https://doi.org/10.2991/mme-16.2017.38How to use a DOI?
- Slow speed slewing bearing, Overturning moment, Fatigue life, Equivalent load, Vibration monitoring.
- Slewing bearings generally consist of the rotational connection between two substructures for combined load at very low speeds. Although there are a large number of traditional or modern techniques widely used in slewing bearing, they may not be able to meet the requirements of bearing industry precisely due to the huge difference between the general bearing and the slewing bearing, thus, the experiments are the most effective and reliable methods. In this paper a special test machine for slewing bearing is presented and an accelerating fatigue life test method based on the test equipment is proposed to predict the whole fatigue life of the slewing bearing. A virtual instrument test system using vibration detection whose hardware was constructed by signal conditioning, data acquisition card and the industrial personal computer was built. Its software platform was based on LabVIEW. Then the hardware configuration, the sensor mounting arrangement and the use of structured and modular approaches to software programming are described. It is shown that the presented test equipment can meet the high reliability requirements of the fatigue life test for slewing bearing under the actual working conditions.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Kang Chen AU - Pei-Tao Yang AU - Guo-Xiang Hou PY - 2016/12 DA - 2016/12 TI - Design of Fatigue Life Testing Machine for Slewing Bearing Based on LabVIEW BT - 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/mme-16.2017.38 DO - https://doi.org/10.2991/mme-16.2017.38 ID - Chen2016/12 ER -