Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review
Structural health monitoring (SHM) methods are essential to guarantee the safety and
integrity of composite structures, which are extensively utilized in aerospace, automobile …
integrity of composite structures, which are extensively utilized in aerospace, automobile …
Advances in prognostics and health management for aircraft landing gear—progress, challenges, and future possibilities
Prognostics and health management (PHM) has developed into a crucial discipline because
of its never-ending pursuit of safety, effectiveness, and dependability. The aircraft Landing …
of its never-ending pursuit of safety, effectiveness, and dependability. The aircraft Landing …
Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network
This paper introduces a novel approach to fault detection in the servo motor bearings of
industrial robots within the context of Industry 4.0 prognostics and health management. The …
industrial robots within the context of Industry 4.0 prognostics and health management. The …
Review on prognostics and health management in smart factory: From conventional to deep learning perspectives
At present, the fourth industrial revolution is pushing factories toward an intelligent,
interconnected grid of machinery, communication systems, and computational resources …
interconnected grid of machinery, communication systems, and computational resources …
Deep transfer learning framework for bearing fault detection in motors
The domain of fault detection has seen tremendous growth in recent years. Because of the
growing demand for uninterrupted operations in different sectors, prognostics and health …
growing demand for uninterrupted operations in different sectors, prognostics and health …
Prognostics and health management of the robotic servo-motor under variable operating conditions
A robot is essential in many industrial and manufacturing facilities due to its efficiency,
accuracy, and durability. However, continuous use of the robotic system can result in various …
accuracy, and durability. However, continuous use of the robotic system can result in various …
MPARN: multi-scale path attention residual network for fault diagnosis of rotating machines
Multi-scale convolutional neural network structures consisting of parallel convolution paths
with different kernel sizes have been developed to extract features from multiple temporal …
with different kernel sizes have been developed to extract features from multiple temporal …
A diagnosis method for imbalanced bearing data based on improved SMOTE model combined with CNN-AM
Z Wang, T Liu, X Wu, C Liu - Journal of Computational Design …, 2023 - academic.oup.com
A boundary enhancement and Gaussian mixture model (G) optimized synthetic minority
oversampling technique (SMOTE) algorithm (BE-G-SMOTE) is proposed to improve …
oversampling technique (SMOTE) algorithm (BE-G-SMOTE) is proposed to improve …
A comprehensive multibody model of a collaborative robot to support model-based health management
Digital models of industrial and collaborative manipulators are widely used for several
applications, such as power-efficient trajectory definition, human–robot cooperation safety …
applications, such as power-efficient trajectory definition, human–robot cooperation safety …
Transfer learning-based intelligent fault detection approach for the industrial robotic system
With increasing customer demand, industry 4.0 gained a lot of interest, which is based on
smart factories. In smart factories, robotic components are vulnerable to failure due to …
smart factories. In smart factories, robotic components are vulnerable to failure due to …