Abstract
The aim of this paper is to study the effect of input parameters choice of the artificial neural network (ANN), in order to obtain the best performances of fault classification. The purpose of this network is to automate the electric motor bearing diagnosis based on vibration signal analysis. The choice of the components of ANN’s inputs (training and testing) has a big challenge for prediction of the machines faults diagnosis. The vibration signals collected from the test rig (Bearing Data Center) are preprocessed, to extract the most appropriate monitoring indicators to analyze the health of the experimental device.
To improve the performance of the neural network, we use three different dataset: the first contains only time indicators, while the second contains the frequency indicators, and the third set is a combination of these two indicators. A comparison between the effects of each feature on the ANN performances, allowed us to choose the optimal structure of input data. The obtained results show that the combined dataset give the best performances compared to the two others dataset.
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Acknowledgment
The authors would like to thank Kenneth A. Loparo, from Bearing Data Center, Case Western Reserve University, Cleveland, for providing us the experimental data.
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Fenineche, H., Felkaoui, A., Rezig, A. (2019). Effect of Input Data on the Neural Networks Performance Applied in Bearing Fault Diagnosis. In: Felkaoui, A., Chaari, F., Haddar, M. (eds) Rotating Machinery and Signal Processing. SIGPROMD’2017 2017. Applied Condition Monitoring, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-319-96181-1_3
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DOI: https://doi.org/10.1007/978-3-319-96181-1_3
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