Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges
J Yang, S Li, Z Wang, H Dong, J Wang, S Tang - Materials, 2020 - mdpi.com
The detection of product defects is essential in quality control in manufacturing. This study
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …
surveys stateoftheart deep-learning methods in defect detection. First, we classify the defects …
A systematic review of deep transfer learning for machinery fault diagnosis
With the popularization of the intelligent manufacturing, much attention has been paid in
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
such intelligent computing methods as deep learning ones for machinery fault diagnosis …
A hybrid generalization network for intelligent fault diagnosis of rotating machinery under unseen working conditions
The data-driven methods in machinery fault diagnosis have become increasingly popular in
the past two decades. However, the wide applications of this scheme are generally …
the past two decades. However, the wide applications of this scheme are generally …
Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review
Z Yang, B Xu, W Luo, F Chen - Measurement, 2022 - Elsevier
With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …
WavCapsNet: An interpretable intelligent compound fault diagnosis method by backward tracking
With significant advantages in feature learning, the deep learning-based compound fault
(CF) diagnosis method has brought many successful applications for industrial equipment; …
(CF) diagnosis method has brought many successful applications for industrial equipment; …
Attitude data-based deep hybrid learning architecture for intelligent fault diagnosis of multi-joint industrial robots
Monitoring the transmission status of multi-joint industrial robots is very important for the
accuracy of the robot motion. The fault diagnosis information is an indispensable basis for …
accuracy of the robot motion. The fault diagnosis information is an indispensable basis for …
A novel self-training semi-supervised deep learning approach for machinery fault diagnosis
Fault diagnosis is an indispensable basis for the collaborative maintenance in prognostic
and health management. Most of existing data-driven fault diagnosis approaches are …
and health management. Most of existing data-driven fault diagnosis approaches are …
Process monitoring for material extrusion additive manufacturing: a state-of-the-art review
A Oleff, B Küster, M Stonis, L Overmeyer - Progress in Additive …, 2021 - Springer
Qualitative uncertainties are a key challenge for the further industrialization of additive
manufacturing. To solve this challenge, methods for measuring the process states and …
manufacturing. To solve this challenge, methods for measuring the process states and …
A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge
Manufacturing quality prediction is one of the significant concerns in modern enterprise
production management, which provides data support for reliability assessment and …
production management, which provides data support for reliability assessment and …
Deep learning in economics: a systematic and critical review
From the perspective of historical review, the methodology of economics develops from
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …