Artificial intelligence systems for tool condition monitoring in machining: Analysis and critical review
The wear of cutting tools, cutting force determination, surface roughness variations and other
machining responses are of keen interest to latest researchers. The variations of these …
machining responses are of keen interest to latest researchers. The variations of these …
Deep learning-driven data curation and model interpretation for smart manufacturing
Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex
production environments, smart manufacturing as envisioned under Industry 4.0 aims to …
production environments, smart manufacturing as envisioned under Industry 4.0 aims to …
Data-driven methods for predictive maintenance of industrial equipment: A survey
W Zhang, D Yang, H Wang - IEEE systems journal, 2019 - ieeexplore.ieee.org
With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on
data-driven methods has become the most effective solution to address smart manufacturing …
data-driven methods has become the most effective solution to address smart manufacturing …
A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals
Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human
analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the …
analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the …
Interpreting network knowledge with attention mechanism for bearing fault diagnosis
Condition monitoring and fault diagnosis of bearings play important roles in production
safety and limiting the cost of maintenance on a reasonable level. Nowadays, artificial …
safety and limiting the cost of maintenance on a reasonable level. Nowadays, artificial …
Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning
Imbalanced data problems are prevalent in the real rotating machinery applications.
Traditional data-driven diagnosis methods fail to identify the fault condition effectively for …
Traditional data-driven diagnosis methods fail to identify the fault condition effectively for …
Machine-learning for automatic prediction of flatness deviation considering the wear of the face mill teeth
The acceptance of the machined surfaces not only depends on roughness parameters but
also in the flatness deviation (Δ fl). Hence, before reaching the threshold of flatness …
also in the flatness deviation (Δ fl). Hence, before reaching the threshold of flatness …
A novel data augmentation approach to fault diagnosis with class-imbalance problem
Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and
safety of industrial systems. However, as a common challenge, the class-imbalance problem …
safety of industrial systems. However, as a common challenge, the class-imbalance problem …
RAP: An end-to-end rate-based congestion control mechanism for realtime streams in the Internet
End-to-end congestion control mechanisms have been critical to the robustness and stability
of the Internet. Most of today's Internet traffic is TCP, and we expect this to remain so in the …
of the Internet. Most of today's Internet traffic is TCP, and we expect this to remain so in the …
Deep focus parallel convolutional neural network for imbalanced classification of machinery fault diagnostics
A Duan, L Guo, H Gao, X Wu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Artificial intelligence-based machinery fault diagnosis techniques have been increasingly
considered in many industrial fields. The convolutional neural network (CNN) is able to learn …
considered in many industrial fields. The convolutional neural network (CNN) is able to learn …