Bearing fault diagnosis based on neural network classification and wavelet transform

Authors:
Omar José Lara Castro;Cristina Castejón Sisamón;Juan Carlos García Prada
Affiliations:
Mechanical Engineering Department, Universidad Carlos III de Madrid, Leganés. Madrid, Spain;Mechanical Engineering Department, Universidad Carlos III de Madrid, Leganés. Madrid, Spain;Mechanical Engineering Department, Universidad Carlos III de Madrid, Leganés. Madrid, Spain
Venue:
WAMUS'06 Proceedings of the 6th WSEAS international conference on Wavelet analysis & multirate systems
Year:
2006

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Abstract

Automated fault classification has been an important pattern recognition problem for decades. In the performance of all motor driven systems, bearings play an important role. The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machinery, in order to provide a significant improvement in plant economy, reduce operational and maintenance costs and improve the level of safety. This paper addresses a recent study to employ Wavelet decomposition to process the accelerometer signals and identify band patterns features. Selected features are extracted from the vibration signatures so obtained and these are used as inputs to three types of artificial networks trained to identify the bearing conditions at three different rotational speeds. Vibration signals for normal bearings, bearing with inner race fault, outer race faults and ball faults were acquired from a motor-driven experimental system. The experimental results are presented and compared with those of currently best-performing on this field. Later sections explain some of the artificial intelligence methods design considerations such as network architecture, performance and implementation. The results demonstrate that the developed diagnostic method can reliably detect and classify four different bearing fault conditions into distinct groups.