Adaptive marginalized stacked denoising autoencoders and its application

In this paper, we propose a modified marginalized autoencoders. Here, the noise adding way at a fixed rate in marginalized autoencoders is replaced by the adaptive noise injection. Compared with the traditional marginalized autoencoders, the proposed method obviously enlarges the recognition perform...

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Bibliographic Details
Published in:Chinese Control Conference pp. 4107 - 4112
Main Authors: Yu, Zhi-bin, Chen, Chun-xia, Pang, Rong, Chen, Tao-wei
Format: Conference Proceeding Journal Article
Language:English
Published: TCCT 01.07.2016
Subjects:
adaptive noise
Conferences
High speed trains
high-speed train
Manifolds
Neurons
Noise
Noise level
Noise reduction
Recognition
stacked autoencoders
Support vector machines
Training
Vibration
Vibrations
Wear
wheel wear state monitoring
Wheels
ISSN:1934-1768
Online Access:Get full text
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Summary:In this paper, we propose a modified marginalized autoencoders. Here, the noise adding way at a fixed rate in marginalized autoencoders is replaced by the adaptive noise injection. Compared with the traditional marginalized autoencoders, the proposed method obviously enlarges the recognition performance. Furthermore, the proposed method is applied to identify high-speed train wheel wear conditions. Features of high speed train wheels wear vibration signals are abstracted by using the adaptive noise marginalized autoencoders, and the features is used to realize the wheel wear characteristics of vibration signal recognitions as the input of support vector machine (SVM). The experimental results show that the accuracy of the new method for identifying high-speed train wheel wear conditions is 99.8% on average.
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ISSN:1934-1768
DOI:10.1109/ChiCC.2016.7553994