Machine learning in materials science
Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …
method and the density functional theory (DFT)‐based method, are unable to keep pace …
Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms
M Jalayer, C Orsenigo, C Vercellis - Computers in Industry, 2021 - Elsevier
Abstract Fault Detection and Diagnosis (FDD) of rotating machinery plays a key role in
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …
Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises
Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …
[HTML][HTML] Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
Predicting residential energy consumption using CNN-LSTM neural networks
The rapid increase in human population and development in technology have sharply
raised power consumption in today's world. Since electricity is consumed simultaneously as …
raised power consumption in today's world. Since electricity is consumed simultaneously as …
[HTML][HTML] Potential, challenges and future directions for deep learning in prognostics and health management applications
Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …
domains, including computer vision and natural language understanding. The drivers for the …
A comprehensive review on convolutional neural network in machine fault diagnosis
With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …
increasingly significant to ensure safe equipment operation and production. Consequently …
A review on deep learning in machining and tool monitoring: Methods, opportunities, and challenges
V Nasir, F Sassani - The International Journal of Advanced Manufacturing …, 2021 - Springer
Data-driven methods provided smart manufacturing with unprecedented opportunities to
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …
facilitate the transition toward Industry 4.0–based production. Machine learning and deep …
[HTML][HTML] A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends
The complex structure of turning aggravates obtaining the desired results in terms of tool
wear and surface roughness. The existence of high temperature and pressure make difficult …
wear and surface roughness. The existence of high temperature and pressure make difficult …
A time series transformer based method for the rotating machinery fault diagnosis
Y Jin, L Hou, Y Chen - Neurocomputing, 2022 - Elsevier
Fault diagnosis of rotating machinery is a significant engineering problem. In recent years,
fault diagnosis methods have matured based on the Convolutional Neural Network (CNN) …
fault diagnosis methods have matured based on the Convolutional Neural Network (CNN) …