Journal ArticleDOI
Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis
TLDR
A novel method is proposed to extract fault features from non-stationary vibration signals of gearboxes using the techniques of signal sparse decomposition and order tracking and an improved matching pursuit algorithm on segmental signal is designed to solve sparse coefficients and reconstruct steady- type fault components and impact-type fault components.About:
This article is published in Measurement.The article was published on 2018-08-01. It has received 87 citations till now. The article focuses on the topics: Order tracking & Fault (power engineering).read more
Citations
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Journal ArticleDOI
A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks
TL;DR: Experimental results demonstrate that the proposed DL-based fault diagnosis method can achieve high diagnosis accuracy under different datasets and present better generalization ability, compared to state-of-the-art fault diagnosis techniques.
Journal ArticleDOI
Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine
TL;DR: A novel fault diagnosis approach integrating Convolutional Neural Networks and Extreme Learning Machine, which can detect different fault types and outperforms other methods in terms of classification accuracy is proposed.
Journal ArticleDOI
Few-shot Transfer Learning for Intelligent Fault Diagnosis of Machine
TL;DR: Few-shot transfer learning method is constructed utilizing meta-learning for few-shot samples diagnosis in variable conditions using a unified 1D convolution network for many-shot diagnosis of three datasets.
Journal ArticleDOI
Deep Semisupervised Domain Generalization Network for Rotary Machinery Fault Diagnosis Under Variable Speed
TL;DR: A deep semisupervised domain generalization network (DSDGN) is proposed for rotary machinery fault diagnosis under variable speed, which can generalize the model to the fault diagnosis task under unseen speed.
Journal ArticleDOI
Domain Adversarial Transfer Network for Cross-Domain Fault Diagnosis of Rotary Machinery
TL;DR: A noveldomain adversarial transfer network (DATN) is proposed, exploiting task-specific feature learning networks and domain adversarial training techniques for handling large distribution discrepancy across domains.
References
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Journal ArticleDOI
Dictionaries for Sparse Representation Modeling
TL;DR: This paper surveys the various options such training has to offer, up to the most recent contributions and structures of the MOD, the K-SVD, the Generalized PCA and others.
Journal ArticleDOI
Condition monitoring and fault diagnosis of planetary gearboxes: A review
TL;DR: This paper aims to review and summarize publications on condition monitoring and fault diagnosis of planetary gearboxes and provide comprehensive references for researchers interested in this topic.
Journal ArticleDOI
A theoretical and experimental investigation of modulation sidebands of planetary gear sets
Murat Inalpolat,Ahmet Kahraman +1 more
TL;DR: In this paper, a simplified mathematical model is proposed to describe the mechanisms leading to modulation sidebands of planetary gear sets, which includes key system parameters such as number of planets, planet position angles, and planet phasing relationships defined by the position angles and the number of teeth of the gears.
Journal ArticleDOI
Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox
TL;DR: In this paper, a sparsity-enabled signal decomposition method was proposed to extract fault features of gearboxes by analyzing the oscillatory behavior of the signal rather than the frequency or scale.
Journal ArticleDOI
Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction
Wei Fan,Gaigai Cai,Gaigai Cai,Zhongkui Zhu,Zhongkui Zhu,Changqing Shen,Weiguo Huang,Li Shang +7 more
TL;DR: In this article, a new transient feature extraction technique is proposed for gearbox fault diagnosis based on sparse representation in wavelet basis, which can extract both the impulse time and the period of transients.
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