How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods

Erkki Jantunen (Corresponding Author)

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)

Abstract

In maintenance it is of greatest importance to know what should be done and when. With condition monitoring it is possible to reduce the number of unplanned stoppages, which cost a lot of money compared to planned maintenance actions. Condition monitoring of rotating machinery, i.e. detection of wear of the components of the machinery, is usually based on indirect methods or monitoring because it is very difficult to monitor or measure wear as such in practise. The reason for this is simply that no such practical methods exist that could be used for measuring the wear of such machinery components as bearings or gears or impellers etc. because these are hidden behind supporting structure or covers. Today the diagnosis of the needed maintenance actions does not usually give a prediction of how much time there is left for maintenance prior the component in question will break. The paper tries to tackle this question in case of rotating machinery and especially in case of rolling bearings. The ultimate goal is to be able to give prognosis of how much time there is before the component will suffer catastrophic failure. The paper starts with a discussion of the wear of rolling element bearings. How does it start, how does it proceed and how does it increase towards the end of the life of the components? The link between the indirect monitoring methods such as oil analysis techniques, vibration measurements and measurement of acoustic emission is covered into some extent. The developed approach starts from the idea of modelling the wear of the component. In case of rotating machinery components the wear often takes place progressively. The reason for this is that when a fault is initiated it increases with increasing speed because the loads that are the cause of wear increase as a function of the size of the fault. In the approach a limited number of condition monitoring parameters are used for diagnosis of the fault. These parameters are then used as input in higher order polynomial regression functions with a limited number of terms. The purpose of using higher order polynomial regression functions is to be able to mimic the development of the fault and also to be able to save the history, i.e. the trend of the development of these parameters, in a very compact form. The regression functions can give prognosis of the development of the fault. The severity of the situation is analyzed using simplified fuzzy logic. A number of measured and analyzed examples are given. All the examples concern rolling bearings, which are probably the most widely monitored component of rotating machinery in the industry. In the tested cases the bearing fault can be diagnosed when about three or four percent of the lifetime of the bearing still remains.
Original languageEnglish
Pages (from-to)24-38
Number of pages15
JournalInternational Journal of COMADEM
Volume9
Issue number3
Publication statusPublished - 2006
MoE publication typeA1 Journal article-refereed

Fingerprint

Bearings (structural)
Condition monitoring
Wear of materials
Rotating machinery
Machinery
Polynomials
Vibration measurement
Impellers
Monitoring
Acoustic emissions
Fuzzy logic
Gears
Oils
Fault

Keywords

  • wear
  • rolling element bearing
  • condition monitoring
  • oil analysis
  • vibration measurement
  • acoustic emission
  • diagnosis
  • prognosis

Cite this

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title = "How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods",
abstract = "In maintenance it is of greatest importance to know what should be done and when. With condition monitoring it is possible to reduce the number of unplanned stoppages, which cost a lot of money compared to planned maintenance actions. Condition monitoring of rotating machinery, i.e. detection of wear of the components of the machinery, is usually based on indirect methods or monitoring because it is very difficult to monitor or measure wear as such in practise. The reason for this is simply that no such practical methods exist that could be used for measuring the wear of such machinery components as bearings or gears or impellers etc. because these are hidden behind supporting structure or covers. Today the diagnosis of the needed maintenance actions does not usually give a prediction of how much time there is left for maintenance prior the component in question will break. The paper tries to tackle this question in case of rotating machinery and especially in case of rolling bearings. The ultimate goal is to be able to give prognosis of how much time there is before the component will suffer catastrophic failure. The paper starts with a discussion of the wear of rolling element bearings. How does it start, how does it proceed and how does it increase towards the end of the life of the components? The link between the indirect monitoring methods such as oil analysis techniques, vibration measurements and measurement of acoustic emission is covered into some extent. The developed approach starts from the idea of modelling the wear of the component. In case of rotating machinery components the wear often takes place progressively. The reason for this is that when a fault is initiated it increases with increasing speed because the loads that are the cause of wear increase as a function of the size of the fault. In the approach a limited number of condition monitoring parameters are used for diagnosis of the fault. These parameters are then used as input in higher order polynomial regression functions with a limited number of terms. The purpose of using higher order polynomial regression functions is to be able to mimic the development of the fault and also to be able to save the history, i.e. the trend of the development of these parameters, in a very compact form. The regression functions can give prognosis of the development of the fault. The severity of the situation is analyzed using simplified fuzzy logic. A number of measured and analyzed examples are given. All the examples concern rolling bearings, which are probably the most widely monitored component of rotating machinery in the industry. In the tested cases the bearing fault can be diagnosed when about three or four percent of the lifetime of the bearing still remains.",
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How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods. / Jantunen, Erkki (Corresponding Author).

In: International Journal of COMADEM, Vol. 9, No. 3, 2006, p. 24-38.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - How to diagnose the wear of rolling element bearings based on indirect condition monitoring methods

AU - Jantunen, Erkki

N1 - Project code: H2SU00063

PY - 2006

Y1 - 2006

N2 - In maintenance it is of greatest importance to know what should be done and when. With condition monitoring it is possible to reduce the number of unplanned stoppages, which cost a lot of money compared to planned maintenance actions. Condition monitoring of rotating machinery, i.e. detection of wear of the components of the machinery, is usually based on indirect methods or monitoring because it is very difficult to monitor or measure wear as such in practise. The reason for this is simply that no such practical methods exist that could be used for measuring the wear of such machinery components as bearings or gears or impellers etc. because these are hidden behind supporting structure or covers. Today the diagnosis of the needed maintenance actions does not usually give a prediction of how much time there is left for maintenance prior the component in question will break. The paper tries to tackle this question in case of rotating machinery and especially in case of rolling bearings. The ultimate goal is to be able to give prognosis of how much time there is before the component will suffer catastrophic failure. The paper starts with a discussion of the wear of rolling element bearings. How does it start, how does it proceed and how does it increase towards the end of the life of the components? The link between the indirect monitoring methods such as oil analysis techniques, vibration measurements and measurement of acoustic emission is covered into some extent. The developed approach starts from the idea of modelling the wear of the component. In case of rotating machinery components the wear often takes place progressively. The reason for this is that when a fault is initiated it increases with increasing speed because the loads that are the cause of wear increase as a function of the size of the fault. In the approach a limited number of condition monitoring parameters are used for diagnosis of the fault. These parameters are then used as input in higher order polynomial regression functions with a limited number of terms. The purpose of using higher order polynomial regression functions is to be able to mimic the development of the fault and also to be able to save the history, i.e. the trend of the development of these parameters, in a very compact form. The regression functions can give prognosis of the development of the fault. The severity of the situation is analyzed using simplified fuzzy logic. A number of measured and analyzed examples are given. All the examples concern rolling bearings, which are probably the most widely monitored component of rotating machinery in the industry. In the tested cases the bearing fault can be diagnosed when about three or four percent of the lifetime of the bearing still remains.

AB - In maintenance it is of greatest importance to know what should be done and when. With condition monitoring it is possible to reduce the number of unplanned stoppages, which cost a lot of money compared to planned maintenance actions. Condition monitoring of rotating machinery, i.e. detection of wear of the components of the machinery, is usually based on indirect methods or monitoring because it is very difficult to monitor or measure wear as such in practise. The reason for this is simply that no such practical methods exist that could be used for measuring the wear of such machinery components as bearings or gears or impellers etc. because these are hidden behind supporting structure or covers. Today the diagnosis of the needed maintenance actions does not usually give a prediction of how much time there is left for maintenance prior the component in question will break. The paper tries to tackle this question in case of rotating machinery and especially in case of rolling bearings. The ultimate goal is to be able to give prognosis of how much time there is before the component will suffer catastrophic failure. The paper starts with a discussion of the wear of rolling element bearings. How does it start, how does it proceed and how does it increase towards the end of the life of the components? The link between the indirect monitoring methods such as oil analysis techniques, vibration measurements and measurement of acoustic emission is covered into some extent. The developed approach starts from the idea of modelling the wear of the component. In case of rotating machinery components the wear often takes place progressively. The reason for this is that when a fault is initiated it increases with increasing speed because the loads that are the cause of wear increase as a function of the size of the fault. In the approach a limited number of condition monitoring parameters are used for diagnosis of the fault. These parameters are then used as input in higher order polynomial regression functions with a limited number of terms. The purpose of using higher order polynomial regression functions is to be able to mimic the development of the fault and also to be able to save the history, i.e. the trend of the development of these parameters, in a very compact form. The regression functions can give prognosis of the development of the fault. The severity of the situation is analyzed using simplified fuzzy logic. A number of measured and analyzed examples are given. All the examples concern rolling bearings, which are probably the most widely monitored component of rotating machinery in the industry. In the tested cases the bearing fault can be diagnosed when about three or four percent of the lifetime of the bearing still remains.

KW - wear

KW - rolling element bearing

KW - condition monitoring

KW - oil analysis

KW - vibration measurement

KW - acoustic emission

KW - diagnosis

KW - prognosis

M3 - Article

VL - 9

SP - 24

EP - 38

JO - International Journal of COMADEM

JF - International Journal of COMADEM

SN - 1363-7681

IS - 3

ER -