Abstract
The rolling element bearing is one of the most critical
components that determine the machinery health and its
remaining lifetime in modern production machinery. Robust
Predictive Health Monitoring tools are needed to
guarantee the healthy state of rolling element bearing s
during the operation. A Predictive Health Monitoring tool
indicates the upcoming failures which provide sufficient
lead time for maintenance planning. The Predictive Health
Monitoring tool aims to monitor the deterioration i.e.
wear evolution rather than just detecting the defects.
The Predictive Health Monitoring procedures contain
detection, diagnosis and prognosis analysis, which are
required to extract the features related to the faulty
rolling element bearing and estimate the remaining useful
lifetime. The purpose of this study is to review the
Predictive Health Monitoring methods and explore their
capabilities, advantages and disadvantage in monitoring
rolling element bearings. Therefore, the study provides a
critical review of the Predictive Health Monitoring
methods of the entire defect evolution process i.e. over
the whole lifetime and suggests enhancements for rolling
element bearing monitoring.
Original language | English |
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Pages (from-to) | 252-272 |
Journal | Mechanical Systems and Signal Processing |
Volume | 60-61 |
DOIs | |
Publication status | Published - 2015 |
MoE publication type | A1 Journal article-refereed |
Keywords
- condition monitoring
- signal analysis
- diagnostics
- prognosis
- dynamic modelling
- rolling bearings