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Erschienen in:

2019 | OriginalPaper | Buchkapitel

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

verfasst von : Hocine Fenineche, Ahmed Felkaoui, Ali Rezig

Erschienen in: Rotating Machinery and Signal Processing

Verlag: 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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Vorheriges Kapitel Intelligent Gear Fault Diagnosis in Normal and Non-stationary Conditions Based on Instantaneous Angular Speed, Differential Evolution and Multi-class Support Vector Machine

Nächstes Kapitel Bearing Diagnostics Using Time-Frequency Filtering and EEMD

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Titel: Effect of Input Data on the Neural Networks Performance Applied in Bearing Fault Diagnosis
verfasst von: Hocine Fenineche
Ahmed Felkaoui
Ali Rezig
Verlag: Springer International Publishing
Buch: Rotating Machinery and Signal Processing
Print ISBN: 978-3-319-96180-4

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

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-319-96181-1_3

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