A systematic review on imbalanced learning methods in intelligent fault diagnosis
The theoretical developments of data-driven fault diagnosis methods have yielded fruitful
achievements and significantly benefited industry practices. However, most methods are …
achievements and significantly benefited industry practices. However, most methods are …
A systematic literature review on transfer learning for predictive maintenance in industry 4.0
The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and
digital technologies within industrial production and manufacturing systems. The objective of …
digital technologies within industrial production and manufacturing systems. The objective of …
Improvement of generative adversarial network and its application in bearing fault diagnosis: A review
A small sample size and unbalanced sample distribution are two main problems when data-
driven methods are applied for fault diagnosis in practical engineering. Technically, sample …
driven methods are applied for fault diagnosis in practical engineering. Technically, sample …
Adversarial deep transfer learning in fault diagnosis: progress, challenges, and future prospects
Y Guo, J Zhang, B Sun, Y Wang - Sensors, 2023 - mdpi.com
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the
superiorities of deep learning in feature representation with the merits of transfer learning in …
superiorities of deep learning in feature representation with the merits of transfer learning in …
An hybrid domain adaptation diagnostic network guided by curriculum pseudo labels for electro-mechanical actuator
Electro-mechanical actuator (EMA) usually operates in complex working conditions. When
develo** data-driven fault diagnosis models for EMA, training and testing data might come …
develo** data-driven fault diagnosis models for EMA, training and testing data might come …
[HTML][HTML] Imbalanced data fault diagnosis method for nuclear power plants based on convolutional variational autoencoding Wasserstein generative adversarial …
J Guo, Y Wang, X Sun, S Liu, B Du - Nuclear Engineering and Technology, 2024 - Elsevier
Data-driven fault diagnosis techniques are significant for the stable operation of nuclear
power plants (NPPs). However, in practical applications, the fault diagnosis of NPPs usually …
power plants (NPPs). However, in practical applications, the fault diagnosis of NPPs usually …
Multi-level weighted dynamic adversarial adaptation network for partial set cross-domain fault diagnosis
Y Zhang, H Zhang, R Wang, B Chen, H Pan - Measurement, 2023 - Elsevier
Abstract Domain adaptation (DA)-based methods have been successfully applied in fault
diagnosis, but their effectiveness relies on the assumption that the source and target label …
diagnosis, but their effectiveness relies on the assumption that the source and target label …
A new method for quantitative estimation of rolling bearings under variable working conditions
Y Yu, X Gu, W Ma, L Guo, H Gao… - … /ASME Transactions on …, 2023 - ieeexplore.ieee.org
Quantitative estimation of fault severity in rolling bearings is crucial for making proper
maintenance decisions. However, bearings usually operate under variable working …
maintenance decisions. However, bearings usually operate under variable working …
Cross-domain fault diagnosis for multimode green ammonia synthesis process based on DA-CycleGAN
Y Hua, W Chen, H **, Q Li, X Ji, Y Dai - Process Safety and Environmental …, 2024 - Elsevier
Green ammonia is a crucial strategy for reducing carbon emissions and promoting
sustainable development. However, in industrial applications, the production load of the …
sustainable development. However, in industrial applications, the production load of the …
Feature decoupling integrated domain generalization network for bearing fault diagnosis under unknown operating conditions
Q **ao, M Yang, J Yan, W Shi - Scientific Reports, 2024 - nature.com
In real engineering scenarios, the complex and variable operating conditions of mechanical
equipment lead to distributional differences between the collected fault data and the training …
equipment lead to distributional differences between the collected fault data and the training …