Use this URL to cite or link to this record in EThOS:
Title: Development of automated bearing condition monitoring using artificial intelligence techniques
Author: Chen, Su Liang
ISNI:       0000 0004 2726 6853
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2009
Availability of Full Text:
Access from EThOS:
Access from Institution:
A recent series of tapered roller bearing tests have been conducted at the University of Southampton to evaluate the effectiveness of using multiple sensing technologies to detect incipient faults. The test rig was instrumented with on-line sensors including vibration, temperature and electrostatic wear and oil-line debris sensors. Off-line techniques were also used such as debris analysis and bearing surface examination. The electrostatic sensors, in particular, have the potential to detect early decay of tribological contacts within rolling element bearings. These sensors have the unique ability to detect surface charge associated with surface phase transformations, material transfer, tribofilm breakdown and debris generation. Thus, they have the capability to detect contact decay before conventional techniques such as vibration and debris monitoring. However, precursor electrostatic events can not always be clearly seen using time and frequency based techniques. Therefore, an intelligent system that can process signals from multiple sensors is needed to enable early and automatic detection of novel events and provide reasoning to these detected anomalies. Operators could then seek corroborative trends between sensors and set robust alarms to ensure safe running. This has been the motivation of this study.
Supervisor: Wood, Robert ; Wang, Ling Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TA Engineering (General). Civil engineering (General)