CLASSIFICATION OF WAVELET MAP PATTERNS USING MULTI-LAYER NEURAL NETWORKS FOR GEAR FAULT DETECTION

2002 ◽  
Vol 16 (4) ◽  
pp. 695-704 ◽  
Author(s):  
D. CHEN ◽  
W.J. WANG
Keyword(s):  
Neural Networks ◽  
Fault Detection ◽  
Gear Fault

A Study on Spiral Bevel Gear Fault Detection Using Artificial Neural Networks and Wavelet Transform

2011 ◽  
Vol 86 ◽  
pp. 214-217
Author(s):  
Bi Bo Fu ◽  
Zong De Fang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wavelet Transform ◽  
Fault Detection ◽  
Bevel Gear ◽  
Spiral Bevel Gear ◽  
Gear Fault ◽  
Artificial Neural ◽  
Spiral Bevel ◽  
Original Time

Based on normal and defective gears of spiral bevel gear pair test, a study is represented to develop the performance of gear fault detection with artificial neural networks and wavelet transform. In order to research the relevant studies of gear failures, a gear fault test rig is designed and constructed, with which vibration test are processed for collecting the signals of a gearbox from this rig. The noise is removed from the original time-domain vibration signals by application of wavelet analysis threshold technique. The extracted energy features from those preprocessed signals are implemented by the wavelet transform, which are used as inputs to the artificial neural networks for two-pattern (normal or fault) recognition. The results show that the represented recognition accuracy of the ANN and WT method for gear fault diagnosis is 100% that is much higher compared with the results of application of ANN separately.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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