<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A methodology based on multi-weights neural network (MWNN) is presented to identify faults of rolling bearing. With considerations of difficulties in analyzing rolling bearing vibration data, we analyzed how to extract time domain feature parameters of faults. Further, the time domain feature parameters extracted from fault signals are utilized to train multi-weights neural network for achieving an optimal coverage of fault feature space. Thus, faults of rolling bearing can be identified. Finally, simulations results based on real sampling data indicate the effectiveness of the methodology proposed in this paper. In addition, simulation results also indicate that MWNN utilized in this paper is more excellent than probabilistic neural network (PNN) and suitable for the classification of small samples.</p></abstract>

A methodology based on multi-weights neural network (MWNN) is presented to identify faults of rolling bearing. With considerations of difficulties in analyzing rolling bearing vibration data, we analyzed how to extract time domain feature parameters of faults. Further, the time domain feature parameters extracted from fault signals are utilized to train multi-weights neural network for achieving an optimal coverage of fault feature space. Thus, faults of rolling bearing can be identified. Finally, simulations results based on real sampling data indicate the effectiveness of the methodology proposed in this paper. In addition, simulation results also indicate that MWNN utilized in this paper is more excellent than probabilistic neural network (PNN) and suitable for the classification of small samples.

Research On The Classification For Faults Of Rolling Bearing Based On Multi-Weights Neural Network

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A methodology based on multi-weights neural network (MWNN) is presented to identify faults of rolling bearing. With considerations of difficulties in analyzing...

Research On The Classification For Faults Of Rolling Bearing Based On Multi-Weights Neural Network

Publié en ligne: 01 sept. 2014

Pages: 1004 - 1023

Reçu: 15 mars 2014

Accepté: 01 sept. 2014

DOI: https://doi.org/10.21307/ijssis-2017-691

Mots clés
time domain parameter, multi-weights neural network, probabilistic neural network (PNN) algorithm, bearing fault classification

© 2014 Yujian Qiang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Research On The Classification For Faults Of Rolling Bearing Based On Multi-Weights Neural Network

Publié en ligne: 01 sept. 2014

Pages: 1004 - 1023

Reçu: 15 mars 2014

Accepté: 01 sept. 2014

DOI: https://doi.org/10.21307/ijssis-2017-691

Mots cléstime domain parameter, multi-weights neural network, probabilistic neural network (PNN) algorithm, bearing fault classification

© 2014 Yujian Qiang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
time domain parameter, multi-weights neural network, probabilistic neural network (PNN) algorithm, bearing fault classification