Deep adversarial capsule network for compound fault diagnosis of machinery toward multidomain generalization task
With advanced measurement technologies and signal analytics algorithms developed
rapidly, the past decades have witnessed large amount of successful breakthroughs and …
rapidly, the past decades have witnessed large amount of successful breakthroughs and …
A universal transfer network for machinery fault diagnosis
Abstract Domain adaptation (DA) methods have achieved promising results in machinery
fault diagnosis owing to their ability to mitigate the distribution discrepancy between …
fault diagnosis owing to their ability to mitigate the distribution discrepancy between …
Classifier inconsistency-based domain adaptation network for partial transfer intelligent diagnosis
Deep networks based mechanical intelligent diagnosis has been recently attracting
considerable attentions with the development of Industry 4.0. Unfortunately, a more practical …
considerable attentions with the development of Industry 4.0. Unfortunately, a more practical …
Deep learning-based machinery fault diagnostics with domain adaptation across sensors at different places
In the recent years, data-driven machinery fault diagnostic methods have been successfully
developed, and the tasks where the training and testing data are from the same distribution …
developed, and the tasks where the training and testing data are from the same distribution …
A novel deep multi-source domain adaptation framework for bearing fault diagnosis based on feature-level and task-specific distribution alignment
B Rezaeianjouybari, Y Shang - Measurement, 2021 - Elsevier
In recent years, deep learning has been extensively applied for intelligent fault diagnosis
systems. Most of the developed algorithms ignore the domain shift problem and assume …
systems. Most of the developed algorithms ignore the domain shift problem and assume …
Machinery fault diagnosis based on domain adaptation to bridge the gap between simulation and measured signals
In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly
dependent on labeled training samples, which may not be obtained in real-world …
dependent on labeled training samples, which may not be obtained in real-world …
Learn generalized features via multi-source domain adaptation: Intelligent diagnosis under variable/constant machine conditions
J Si, H Shi, T Han, J Chen, C Zheng - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
A primary goal of fault diagnosis is to build generalizable models for flexible industrial
scenes. However, most literature assumed that the training and testing data are collected …
scenes. However, most literature assumed that the training and testing data are collected …
A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions
T Gao, J Yang, Q Tang - Information Fusion, 2024 - Elsevier
In practical industrial scenarios, the variations of operating conditions such as load and
rotational speed make mechanical systems subject to complex and variable environmental …
rotational speed make mechanical systems subject to complex and variable environmental …
Partial domain adaptation method based on class-weighted alignment for fault diagnosis of rotating machinery
X Zhang, J Wang, S Jia, B Han… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Domain adaptation (DA)-based methods for fault diagnosis (FD) of rotating machinery have
achieved impressive results in recent years. Most methods hold the assumption that the …
achieved impressive results in recent years. Most methods hold the assumption that the …
Conditional contrastive domain generalization for fault diagnosis
Data-driven fault diagnosis plays a key role in stability and reliability of operations in modern
industries. Recently, deep learning has achieved remarkable performance in fault …
industries. Recently, deep learning has achieved remarkable performance in fault …