The scale effect on condition monitoring of large scale rolling bearings

Idriss El-Thalji, Erkki Jantunen

Research output: Contribution to conferenceConference articleScientificpeer-review

Abstract

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.
Original languageEnglish
Number of pages10
Publication statusPublished - 2014
Event1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14 - Amman, Jordan
Duration: 11 Nov 201413 Nov 2014
Conference number: 1

Conference

Conference1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14
Abbreviated titleISME'14
CountryJordan
CityAmman
Period11/11/1413/11/14

Fingerprint

Bearings (structural)
Condition monitoring
Health
Machinery
Monitoring
Topology
Planning
Defects
Costs

Keywords

  • condition monitoring
  • dynamic modelling
  • contact mechanics
  • wear evolution
  • rolling bearing

Cite this

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.
El-Thalji, Idriss ; Jantunen, Erkki. / 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.10 p.
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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, 11/11/14 - 13/11/14, .

The scale effect on condition monitoring of large scale rolling bearings. / El-Thalji, Idriss; Jantunen, Erkki.

2014. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.

Research output: Contribution to conferenceConference articleScientificpeer-review

TY - CONF

T1 - The scale effect on condition monitoring of large scale rolling bearings

AU - El-Thalji, Idriss

AU - Jantunen, Erkki

N1 - Project code: 76753

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - condition monitoring

KW - dynamic modelling

KW - contact mechanics

KW - wear evolution

KW - rolling bearing

M3 - Conference article

ER -

El-Thalji I, Jantunen E. The scale effect on condition monitoring of large scale rolling bearings. 2014. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.