Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected sig...

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Vydáno v:Shock and vibration Ročník 2020; číslo 2020; s. 1 - 10
Hlavní autoři: Fu, Chao, Lin, Hsiung-Cheng, Lv, Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Adaptive algorithms
Algorithms
Artificial neural networks
Classification
Convolution
Fast Fourier transformations
Fault diagnosis
Feature extraction
Image retrieval
Load fluctuation
Multilayer perceptrons
Neural networks
Neurons
Rotating machinery
Support vector machines
Vibration
ISSN:1070-9622, 1875-9203
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Shrnutí:It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. They, however, may suffer from time-consuming and low versatility. In this paper, a CNN integrated with the adaptive batch normalization (ABN) algorithm (ABN-CNN) is developed to avoid high computing resource requirements of such complex networks. It uses a large-scale convolution kernel at the grassroots level and a multidimensional 3 × 1 small convolution nuclear. Therefore, a fast convergence and high recognition accuracy under noise and load variation environment can be achieved for bearing fault diagnosis. The performance results verify that the proposed model is superior to Support Vector Machine with Fast Fourier Transform (FFT-SVM) and Multilayer Perceptron with Fast Fourier Transform (FFT-MLP) models and Deep Neural Network with Fast Fourier Transform (FFT-DNN).
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ISSN:1070-9622
1875-9203
DOI:10.1155/2020/8837958