Journal ArticleDOI
Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines
Xiaoyuan Zhang,Jianzhong Zhou +1 more
TLDR
The results show that the proposed method outperforms other methods both mentioned in this paper and published in other literatures.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 2013-12-01. It has received 245 citations till now. The article focuses on the topics: Bearing (mechanical) & Singular value decomposition.read more
Citations
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Journal ArticleDOI
Artificial intelligence for fault diagnosis of rotating machinery: A review
TL;DR: This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications.
Journal ArticleDOI
Applications of machine learning to machine fault diagnosis: A review and roadmap
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
Journal ArticleDOI
Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis
TL;DR: A novel hierarchical learning rate adaptive deep convolution neural network based on an improved algorithm that is well suited to the fault-diagnosis model and superior to other existing methods is proposed.
Journal ArticleDOI
A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM
TL;DR: In this paper, a hybrid model for fault detection and classification of motor bearing is presented, where the permutation entropy (PE) of the vibration signal is calculated to detect the malfunctions of the bearing.
Journal ArticleDOI
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
Meng Gan,Cong Wang,Chang׳an Zhu +2 more
TL;DR: The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances.
References
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Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
TL;DR: In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.
Journal ArticleDOI
Ensemble empirical mode decomposition: a noise-assisted data analysis method
Zhaohua Wu,Norden E. Huang +1 more
TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Journal ArticleDOI
Choosing Multiple Parameters for Support Vector Machines
TL;DR: The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.
Journal ArticleDOI
Support vector machine in machine condition monitoring and fault diagnosis
Achmad Widodo,Bo-Suk Yang +1 more
TL;DR: This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM), and attempts to summarize and review the recent research and developments of SVM in machine condition Monitoring and diagnosis.
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