Vibration condition monitoring and fault classification of rolling element bearings utilising Kohonen's self-organising maps

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dc.contributor.advisor Enslin, J.
dc.contributor.advisor Van der Merwe, D. F. Nkuna, Jay Shipalani Rhulani 2013-08-21T13:26:50Z 2013-08-21T13:26:50Z 2013-08-21 2006-09
dc.description Thesis. (M. Tech. (Mechanical Engineering))--Vaal University of Technology en
dc.description.abstract Bearing condition monitoring and fault diagnosis have been studied for many years. Popular techniques are applied through advanced signal processing and pattern recognition technologies. The subject of the research was vibration condition monitoring of incipient damage in rolling element bearings. The research was confined to deep-groove ball bearings because of their common applications in industry. The aim of the research was to apply neural networks to vibration condition monitoring of rolling element bearings. Kohonen's Self-Organising Feature Map is the neural network that was used to enable an automatic condition monitoring system. Bearing vibration is induced during bearing operation and the main cause is bearing friction, which ultimately causes wear and incipient spalling in a rolling element bearing. To obtain rolling element bearing vibrations a condition monitoring test rig for rolling element bearings had to be designed and built. A digital vibration measurement acquisition environment was created in Labview and Matlab. Data from the bearing test rig was recorded with a piezoelectric accelerometer, and an S-type load cell connected to dynamic signal analysis cards. The vibration measurement instrumentation was cost-effective and yielded accurate and repeatable measurements. Defects on rolling element bearings were artificially inflicted so that a pattern of bearing defects could be established. An input data format of vibration statistical parameters was created using the time and frequency domain signals. Kohonen's Self-Organising Feature Maps were trained in the input data, utilising an unsupervised, competitive learning algorithm and vector quantisation to cluster the bearing defects on a two-dimensional topographical map. A new practical dimension to condition monitoring of rolling element bearings was developed. The use of time domain and frequency domain analysis of bearing vibration has been combined with a visual and classification analysis of distinct bearing defects through the application of the Self-Organising Feature Map. This is a suitable technique for rolling element bearing defect detection, remaining bearing life estimation and to assist in planning maintenance schedules. en
dc.description.sponsorship National Research Foundation; Council for Scientific and Industrial Research en
dc.format.extent xix,160 leaves: bill. en
dc.language.iso en en
dc.relation.requires Pdf. Adobe Acrobat Reader en
dc.subject Bearing condition monitoring en
dc.subject Rolling element bearings en
dc.subject Vibration condition monitoring en
dc.subject Kohonen's Self-organising feature map en
dc.subject Bearing defects en
dc.subject.ddc 620.3 en
dc.subject.lcsh Bearings (Machinery) -- Vibration. en
dc.subject.lcsh Bearings (Machinery) en
dc.title Vibration condition monitoring and fault classification of rolling element bearings utilising Kohonen's self-organising maps en
dc.type Thesis en

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