Journal ArticleDOI
A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree
TLDR
The rolling bearing fault diagnosis method based on LMD, MPE, LS and ISVM-BT is proposed and the experimental results indicate the proposed method is effective in identifying the different categories of rolling bearings.About:Â
This article is published in Measurement.The article was published on 2016-01-01. It has received 221 citations till now. The article focuses on the topics: Feature extraction & Feature (machine learning).read more
Citations
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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
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
TL;DR: A novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN), which can not only achieve 100% classification accuracy on normal signals, but also outperform the state-of-the-art DNN model which is based on frequency features under different working load and noisy environment conditions.
Journal ArticleDOI
A survey on Deep Learning based bearing fault diagnosis
Duy-Tang Hoang,Hee-Jun Kang +1 more
TL;DR: The three popular Deep Learning algorithms for Bearing fault diagnosis including Autoencoder, Restricted Boltzmann Machine, and Convolutional Neural Network are briefly introduced and their applications are reviewed through publications and research works on the area of bearing fault diagnosis.
Journal ArticleDOI
A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing
Xiaoan Yan,Minping Jia +1 more
TL;DR: Experimental results show that the proposed fault classification algorithm achieves high diagnosis accuracy for different working conditions of rolling bearing and outperforms some traditional methods both mentioned in this paper and published in other literature.
Journal ArticleDOI
A New Feature Extraction Method Based on EEMD and Multi-Scale Fuzzy Entropy for Motor Bearing
TL;DR: The experiment results show that the proposed EDOMFE method can effectively extract fault features from the vibration signal and the proposed EOMSMFD method can accurately diagnose the fault types and fault severities for the inner race fault, the outerRace fault, and rolling element fault of the motor bearing.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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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?
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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.
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Physiological time-series analysis using approximate entropy and sample entropy
TL;DR: A new and related complexity measure is developed, sample entropy (SampEn), and a comparison of ApEn and SampEn is compared by using them to analyze sets of random numbers with known probabilistic character, finding SampEn agreed with theory much more closely than ApEn over a broad range of conditions.
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Permutation entropy: a natural complexity measure for time series.
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