A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion
出版年份 2022 全文链接
标题
A hierarchical deep learning framework for combined rolling bearing fault localization and identification with data fusion
作者
关键词
-
出版物
JOURNAL OF VIBRATION AND CONTROL
Volume -, Issue -, Pages 107754632210916
出版商
SAGE Publications
发表日期
2022-04-27
DOI
10.1177/10775463221091601
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Intelligent Fault Diagnosis of Rolling Bearings Based on Refined Composite Multi-Scale Dispersion q-Complexity and Adaptive Whale Algorithm-Extreme Learning Machine
- (2021) Wei Dong et al. MEASUREMENT
- Harnessing fuzzy neural network for gear fault diagnosis with limited data labels
- (2021) Kai Zhou et al. The International Journal of Advanced Manufacturing Technology
- Experimental investigation on time-domain features in the diagnosis of rolling element bearings by acoustic emission
- (2021) Aref Aasi et al. JOURNAL OF VIBRATION AND CONTROL
- Harnessing deep learning for physics-informed prediction of composite strength with microstructural uncertainties
- (2021) Kai Zhou et al. COMPUTATIONAL MATERIALS SCIENCE
- Single and Multi-label Fault Classification in rotors from unprocessed multi-sensor data through deep and parallel CNN architectures
- (2021) Nikhil A. Sonkul et al. EXPERT SYSTEMS WITH APPLICATIONS
- Probabilistic bearing fault diagnosis using Gaussian process with tailored feature extraction
- (2021) Mingxuan Liang et al. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
- A novel ResNet-based model structure and its applications in machine health monitoring
- (2020) Jian Duan et al. JOURNAL OF VIBRATION AND CONTROL
- Integrated condition monitoring scheme for bearing fault diagnosis of a wind turbine gearbox
- (2019) Vamsi Inturi et al. JOURNAL OF VIBRATION AND CONTROL
- In-Process Quality Inspection of Rolling Element Bearings Based on the Measurement of Microelastic Deformation of Outer Ring
- (2019) Kuosheng Jiang et al. SHOCK AND VIBRATION
- Deep semi-supervised generative adversarial fault diagnostics of rolling element bearings
- (2019) David Benjamin Verstraete et al. STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
- A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
- (2019) Meidi Sun et al. MEASUREMENT
- Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery
- (2018) Liuyang Song et al. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
- Combined deep belief network in deep learning with affinity propagation clustering algorithm for roller bearings fault diagnosis without data label
- (2018) Fan Xu et al. JOURNAL OF VIBRATION AND CONTROL
- A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
- (2018) Wei Zhang et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis
- (2018) Yuanhang Chen et al. NEUROCOMPUTING
- An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM
- (2018) STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING
- Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network
- (2018) Abbas Rohani Bastami et al. Iranian Journal of Science and Technology-Transactions of Electrical Engineering
- Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation
- (2018) Xiang Li et al. JOURNAL OF INTELLIGENT MANUFACTURING
- A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
- (2017) Long Wen et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals
- (2016) Jinglong Chen et al. RENEWABLE ENERGY
- Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy
- (2016) Lei Zhang et al. SHOCK AND VIBRATION
- Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals
- (2015) Jaouher Ben Ali et al. APPLIED ACOUSTICS
- Using supervised kernel entropy component analysis for fault diagnosis of rolling bearings
- (2015) Hongdi Zhou et al. JOURNAL OF VIBRATION AND CONTROL
- Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN
- (2013) D.H. Pandya et al. EXPERT SYSTEMS WITH APPLICATIONS
- Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses
- (2010) E.P. de Moura et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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