A sensitivity comparison of Neuro-fuzzy feature extraction methods from bearing failure signals
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This thesis presents an account of investigations made into building bearing fault classifiers for outer race faults (ORF), inner race faults (IRF), ball faults (BF) and no fault (NF) cases using wavelet transforms, statistical parameter features and Artificial Neuro-Fuzzy Inference Systems (ANFIS). The test results showed that the ball fault (BF) classifier successfully achieved 100% accuracy without mis-classification, while the outer race fault (ORF), inner race fault (IRF) and no fault (NF) classifiers achieved mixed results.
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Xue, S.; Howard, Ian ; Wang, C.; Bao, H.; Lian, P.; Chen, G.; Wang, Y.; Yan, Y. (2019)© 2019 Elsevier Ltd This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planet bearing fault detection. The inner race of the planet bearing is connected to ...
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