Abstract
This paper proposes a novel cyclic statistics based artificial neural network for early fault diagnosis of rolling element bearing, via which the real time domain signals obtained from a test rig are preprocessed by cyclic statistics to perform monitoring fault diagnosis. Three kinds of familiar faults are intentionally introduced in order to investigate typical rolling element bearing faults. The testing results are presented and discussed with examples of real time data collected from the test rig.
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© 2004 Springer-Verlag Berlin Heidelberg
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Zhou, F., Chen, J., He, J., Bi, G., Zhang, G., Li, F. (2004). Cyclic Statistics Based Neural Network for Early Fault Diagnosis of Rolling Element Bearings. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_95
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DOI: https://doi.org/10.1007/978-3-540-28648-6_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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