A review of the application of deep learning in intelligent fault diagnosis of rotating machinery
Z Zhu, Y Lei, G Qi, Y Chai, N Mazur, Y An, X Huang - Measurement, 2023 - Elsevier
With the rapid development of industry, fault diagnosis plays a more and more important role
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
in maintaining the health of equipment and ensuring the safe operation of equipment. Due to …
Computational principles and challenges in single-cell data integration
The development of single-cell multimodal assays provides a powerful tool for investigating
multiple dimensions of cellular heterogeneity, enabling new insights into development …
multiple dimensions of cellular heterogeneity, enabling new insights into development …
Mapping single-cell data to reference atlases by transfer learning
Large single-cell atlases are now routinely generated to serve as references for analysis of
smaller-scale studies. Yet learning from reference data is complicated by batch effects …
smaller-scale studies. Yet learning from reference data is complicated by batch effects …
Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review
A smart factory is a highly digitized and connected production facility that relies on smart
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
manufacturing. Additionally, artificial intelligence is the core technology of smart factories …
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …
tremendous progress, which can help reduce costly breakdowns. However, different …
A novel deep convolution multi-adversarial domain adaptation model for rolling bearing fault diagnosis
L Wan, Y Li, K Chen, K Gong, C Li - Measurement, 2022 - Elsevier
The traditional rolling bearing fault diagnosis methods are difficult to achieve effective cross-
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …
domain fault diagnosis. Therefore, a novel deep convolution multi-adversarial domain …
Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects
Y Feng, J Chen, J Xie, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …
strong capability of automatic feature extraction and accurate identification for fault signals …
Deep transfer learning for bearing fault diagnosis: A systematic review since 2016
The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …
training and test data follow the same distribution. This assumption, however, is not always …
Applications of unsupervised deep transfer learning to intelligent fault diagnosis: A survey and comparative study
Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …
representation learning and plenty of labeled data. However, machines often operate with …
Condition monitoring based on partial discharge diagnostics using machine learning methods: A comprehensive state-of-the-art review
This paper presents a state-of-the-art review on machine learning (ML) based intelligent
diagnostics that have been applied for partial discharge (PD) detection, localization, and …
diagnostics that have been applied for partial discharge (PD) detection, localization, and …