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Feature Selection Scheme Based on Pareto Method for Gearbox Fault Diagnosis Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Effect of Input Data on the Neural Networks Performance Applied in Bearing Fault Diagnosis

Authors : Hocine Fenineche, Ahmed Felkaoui, Ali Rezig

Published in: Rotating Machinery and Signal Processing

Publisher: Springer International Publishing

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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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previous chapter Intelligent Gear Fault Diagnosis in Normal and Non-stationary Conditions Based on Instantaneous Angular Speed, Differential Evolution and Multi-class Support Vector Machine
next chapter Bearing Diagnostics Using Time-Frequency Filtering and EEMD
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Metadata
Title
Effect of Input Data on the Neural Networks Performance Applied in Bearing Fault Diagnosis
Authors
Hocine Fenineche
Ahmed Felkaoui
Ali Rezig
Copyright Year
2019
Book
Rotating Machinery and Signal Processing

Print ISBN: 978-3-319-96180-4

Electronic ISBN: 978-3-319-96181-1

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-96181-1_3

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