A review on vibration-based condition monitoring of rotating machinery
Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal
functioning states are related to specific patterns that can be extracted from vibration signals …
functioning states are related to specific patterns that can be extracted from vibration signals …
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 …
Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach
C Wang - Information Processing & Management, 2022 - Elsevier
Abstract Nowadays, Artificial Intelligence (AI) based modeling is the major consideration to
build efficient, automated, and smart systems for our today's needs. Many companies are …
build efficient, automated, and smart systems for our today's needs. Many companies are …
Prediction performance analysis of neural network models for an electrical discharge turning process
In many of the modern-day manufacturing industries, electrical discharge machining (EDM)
now appears as an effective non-traditional material removal process for generating intricate …
now appears as an effective non-traditional material removal process for generating intricate …
A new deep learning framework for imbalance detection of a rotating shaft
M Wisal, KY Oh - Sensors, 2023 - mdpi.com
Rotor unbalance is the most common cause of vibration in industrial machines. The
unbalance can result in efficiency losses and decreased lifetime of bearings and other …
unbalance can result in efficiency losses and decreased lifetime of bearings and other …
A fault diagnosis method for nuclear power plant rotating machinery based on adaptive deep feature extraction and multiple support vector machines
W Yin, H Xia, X Huang, J Zhang… - Progress in Nuclear Energy, 2023 - Elsevier
Rotating machinery is the essential component in nuclear power plants (NPPs). Effective
fault detection and diagnosis is a main challenge in the operation and maintenance of NPPs …
fault detection and diagnosis is a main challenge in the operation and maintenance of NPPs …
Advanced dual RNN architecture for electrical motor fault classification
In recent years, there has been a remarkable increase in the usage of Deep Neural
Networks (DNNs) for addressing and solving electrical field problems. This research …
Networks (DNNs) for addressing and solving electrical field problems. This research …
A real-time defect detection in printed circuit boards applying deep learning
Inspection of defects in the printed circuit boards (PCBs) has both safety and economic
significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem …
significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem …
Monitoring Big Data Streams Using Data Stream Management Systems: Industrial Needs, Challenges, and Improvements
A Alzghoul - Advances in Operations Research, 2023 - Wiley Online Library
Real‐time monitoring systems are important for industry since they allow for avoiding
unplanned system stops and keeping system availability high. The technical requirements …
unplanned system stops and keeping system availability high. The technical requirements …
A deep learning approach to predict the flow field and thermal patterns of nonencapsulated phase change materials suspensions in an enclosure
M Edalatifar, MB Tavakoli, F Setoudeh - Journal of Applied and …, 2022 - jacm.scu.ac.ir
The flow and heat transfer of a novel type of functional phase change nanofluids, nano-
encapsulated phase change suspensions, is investigated in the present study using a deep …
encapsulated phase change suspensions, is investigated in the present study using a deep …