Search Results - "Ding, Chuancang"

Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis by Que, Hongbo, Liu, Xuyan, Jin, Siqin, Huo, Yaoyan, Wu, Chengpan, Ding, Chuancang, Zhu, Zhongkui

“…Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the…”
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Dynamic Modeling and Analysis of an RV Reducer Considering Tooth Profile Modifications and Errors by Li, Xuan, Huang, Jiaqing, Ding, Chuancang, Guo, Ran, Niu, Weilong

“…Due to their advantages of compact size, high reduction ratio, large stiffness and high load capacity, RV reducers have been widely used in industrial robots…”
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Hierarchical Frequency-Domain Sparsity-Based Algorithm for Fault Feature Extraction of Rolling Bearings by Wang, Baoxiang, Ding, Chuancang

“…Rolling bearings are the crucial parts in rotating machines and its fault detection is indispensable for ensuring operational reliability of entire mechanical…”
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Deep Coupled Dense Convolutional Network With Complementary Data for Intelligent Fault Diagnosis by Jiao, Jinyang, Zhao, Ming, Lin, Jing, Ding, Chuancang

“…In recent years, artificial intelligent techniques have been extensively explored in the field of health monitoring and fault diagnosis due to their powerful…”
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Sparsity-assisted adaptive chirp mode decomposition and its application in rub-impact fault detection by Ding, Chuancang, Wang, Baoxiang

“…•The sparsity-assisted IF update scheme exploiting sparse prior is introduced.•The S-ACMD is proposed for extracting oscillating IF and detecting rub-impact…”
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Transient feature identification from internal encoder signal for fault detection of planetary gearboxes under variable speed conditions by Wang, Baoxiang, Ding, Chuancang

“…•LpfSpaA is developed for fault-induced IAS fluctuation extraction.•An iterative algorithm is derived and a parameter selection strategy is constructed.•The…”
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Classifier Inconsistency-Based Domain Adaptation Network for Partial Transfer Intelligent Diagnosis by Jiao, Jinyang, Zhao, Ming, Lin, Jing, Ding, Chuancang

“…Deep networks based mechanical intelligent diagnosis has been recently attracting considerable attentions with the development of Industry 4.0. Unfortunately,…”
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Maximum average kurtosis deconvolution and its application for the impulsive fault feature enhancement of rotating machinery by Liang, Kaixuan, Zhao, Ming, Lin, Jing, Jiao, Jinyang, Ding, Chuancang

“…•MAKD is proposed to solve the deconvolution problem under complex working conditions.•Morlet wavelet is used as initial filter to improve efficiency and…”
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Sparsity-Based Algorithm for Condition Assessment of Rotating Machinery Using Internal Encoder Data by Ding, Chuancang, Zhao, Ming, Lin, Jing, Jiao, Jinyang, Liang, Kaixuan

“…This article proposes a novel three-stage condition assessment scheme of rotating machinery using internal encoder data rather than traditional external…”
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Cycle-consistent Adversarial Adaptation Network and its application to machine fault diagnosis by Jiao, Jinyang, Lin, Jing, Zhao, Ming, Liang, Kaixuan, Ding, Chuancang

“…Driven by industrial big data and intelligent manufacturing, deep learning approaches have flourished and yielded impressive achievements in the community of…”
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Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings by Ding, Chuancang, Zhao, Ming, Lin, Jing, Jiao, Jinyang

“…Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the…”
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Towards Prediction Constraints: A Novel Domain Adaptation Method for Machine Fault Diagnosis by Jiao, Jinyang, Liang, Kaixuan, Ding, Chuancang, Lin, Jing

“…Domain adaptation technologies have been extensively explored and successfully applied to machine fault diagnosis, aiming to address problems that target data…”
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A mixed adversarial adaptation network for intelligent fault diagnosis by Jiao, Jinyang, Zhao, Ming, Lin, Jing, Liang, Kaixuan, Ding, Chuancang

“…Behind the brilliance of the deep diagnosis models, the issue of distribution discrepancy between source training data and target test data is being gradually…”
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A novel domain generalization network with multidomain specific auxiliary classifiers for machinery fault diagnosis under unseen working conditions by Wang, Rui, Huang, Weiguo, Lu, Yixiang, Zhang, Xiao, Wang, Jun, Ding, Chuancang, Shen, Changqing

“…•A novel DGNet_MSAC is proposed for cross-domain generalization fault diagnosis.•Multiple domain-specific auxiliary classifiers are designed in the…”
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Toothwise Health Monitoring of Planetary Gearbox Under Time-Varying Speed Condition Based on Rotating Encoder Signal by Liang, Kaixuan, Zhao, Ming, Lin, Jing, Jiao, Jinyang, Ding, Chuancang

“…To meet industrial demand, plenty of research works have been dedicated to monitoring the health status of planetary gearboxes. For the same purpose, a new…”
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Multiweight Adversarial Open-Set Domain Adaptation Network for Machinery Fault Diagnosis With Unknown Faults by Wang, Rui, Huang, Weiguo, Shi, Mingkuan, Ding, Chuancang, Wang, Jun

“…Domain adaptation (DA) methods have proven successful in addressing the domain-shift challenge in rotating machinery fault diagnosis, and the basic tasks that…”
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Physics-informed unsupervised domain adaptation framework for cross-machine bearing fault diagnosis by Jia, Ning, Huang, Weiguo, Ding, Chuancang, Wang, Jun, Zhu, Zhongkui

“…Varying components and operating conditions in industrial machines lead to different distribution characteristics and fault states of monitoring data for…”
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Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis by Shi, Mingkuan, Ding, Chuancang, Wang, Rui, Song, Qiuyu, Shen, Changqing, Huang, Weiguo, Zhu, Zhongkui

“…Intelligent fault diagnosis based on deep learning (DL) has been widely used in various engineering practices. However, when confronting massive unlabeled…”
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Graph embedding deep broad learning system for data imbalance fault diagnosis of rotating machinery by Shi, Mingkuan, Ding, Chuancang, Wang, Rui, Shen, Changqing, Huang, Weiguo, Zhu, Zhongkui

“…•The supervised GEBLSAE capable of extracting feature representations with stronger class discrimination is proposed.•The traditional unsupervised autoencoder…”
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Imbalanced class incremental learning system: A task incremental diagnosis method for imbalanced industrial streaming data by Shi, Mingkuan, Ding, Chuancang, Shen, Changqing, Huang, Weiguo, Zhu, Zhongkui

“…In recent years, machine learning has been widely used in various fault diagnosis scenarios. However, existing machine learning algorithms tend to work well in…”
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