A summary of fault modelling and predictive health monitoring of rolling element bearings. (August 2015)

• Record Type: Journal Article
• Authors: El-Thalji, Idriss; Jantunen, Erkki
• Is Part Of: Mechanical systems and signal processing. Volume 60/61(2015)
• Page Start: 252
• Page End: 272
• Publication Date: 2015-08
• Subjects: Condition monitoring -- Signal analysis -- Diagnostics -- Prognosis -- Dynamic modelling -- Rolling bearings; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.02.008
• Languages: English
• ISSNs: 0888-3270

Abstract

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. Highlights: A review of Predictive health monitoring for rolling bearings is provided. A review of fault modelling for rolling bearings is provided. A discussion of wear evolution, surface topology evolution and their influences on the predictive health monitoring process is illustrated.