Vibration condition monitoring and fault classification of rolling element bearings utilising Kohonen's self-organising maps
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Date
2013-08-21
Authors
Nkuna, Jay Shipalani Rhulani
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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.
Description
Thesis. (M. Tech. (Mechanical Engineering))--Vaal University of Technology
Keywords
Bearing condition monitoring, Rolling element bearings, Vibration condition monitoring, Kohonen's Self-organising feature map, Bearing defects