相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Guoqian Jiang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprec's on Manufacturing
Chengming Shi et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification
Xin-Cheng Cao et al.
COMPUTERS IN INDUSTRY (2019)
In-process complex machining condition monitoring based on deep forest and process information fusion
Zhiyuan Lu et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)
A novel method for tool condition monitoring based on long short-term memory and hidden Markov model hybrid framework in high-speed milling Ti-6Al-4V
Zhengrui Tao et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
Huihui Qiao et al.
IEEE ACCESS (2019)
Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
Liangzhi Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
Cloud-based manufacturing process monitoring for smart diagnosis services
Alessandra Caggiano
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING (2018)
Cloud-Based Parallel Machine Learning for Tool Wear Prediction
Dazhong Wu et al.
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2018)
Predicting tool wear with multi-sensor data using deep belief networks
Yuxuan Chen et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2018)
A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series
Huihui Qiao et al.
SENSORS (2018)
Analysis of tri-axial force and vibration sensors for detection of failure criterion in deep twist drilling process
M. H. S. Harun et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)
A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction
Jinsong Yu et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)
Force-based tool condition monitoring for turning process using v-support vector regression
Ning Li et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)
A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing
Dazhong Wu et al.
JOURNAL OF MANUFACTURING SYSTEMS (2017)
Fuzzy logic based tool condition monitoring for end-milling
Besmir Cuka et al.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2017)
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Rui Zhao et al.
SENSORS (2017)
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
Yaguo Lei et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2016)
Cloud-enabled prognosis for manufacturing
R. Gao et al.
CIRP ANNALS-MANUFACTURING TECHNOLOGY (2015)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear
C Scheffer et al.
NEURAL COMPUTING & APPLICATIONS (2005)