The predictive maintenance is a key factor to maintain the industrial assets at the ultimate cost effective level. Predictive health monitoring tools are needed to guarantee the healthy state of critical machineries during the operation. Predictive health monitoring tools indicate the upcoming failures providing longer lead time for maintenance planning. The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. The scale of modern machineries is rapidly growing and leading to several critical lifetime challenges. At the same time, it is hard to monitor the faults of large scale bearings. Therefore, the purpose of this paper is to provide an illustration of the scale effect of the effectiveness of condition monitoring tools. It is shown that the scale ratio between the rolling element and the defect topology play a significant role in affecting the overall dynamic behaviour of the system beside the significant effect of the rotational speed. The study illustrates the difficulties to monitor large scale bearings. The benefit of such phenomenal illustration is to enhance the condition monitoring tools for large scale bearings.
|Number of pages||10|
|Publication status||Published - 2014|
|Event||1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14 - Amman, Jordan|
Duration: 11 Nov 2014 → 13 Nov 2014
Conference number: 1
|Conference||1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14|
|Period||11/11/14 → 13/11/14|
- condition monitoring
- dynamic modelling
- contact mechanics
- wear evolution
- rolling bearing
El-Thalji, I., & Jantunen, E. (2014). The scale effect on condition monitoring of large scale rolling bearings. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.