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。A novel deep learning method based on attention mechanism for bearing remaining useful life prediction
Yuanhang Chen et al.
APPLIED SOFT COMPUTING (2020)
Modeling and analysis of tool wear prediction based on SVD and BiLSTM
Xiaoqiang Wu et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)
A hybrid information model based on long short-term memory network for tool condition monitoring
Weili Cai et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2020)
Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion
Xingwei Xu et al.
MEASUREMENT (2020)
Combining translation-invariant wavelet frames and convolutional neural network for intelligent tool wear state identification
Xin-Cheng Cao et al.
COMPUTERS IN INDUSTRY (2019)
Research on the milling tool wear and life prediction by establishing an integrated predictive model
Yinfei Yang et al.
MEASUREMENT (2019)
Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals
Chengdong Wang et al.
MEASUREMENT (2019)
A deep convolutional neural networks model for intelligent fault diagnosis of a gearbox under different operational conditions
Guangqi Qiu et al.
MEASUREMENT (2019)
Study of a tap failure at the internal threads machining
Peter Monka et al.
ENGINEERING FAILURE ANALYSIS (2019)
Investigation of progressive tool wear for determining of optimized machining parameters in turning
Mustafa Kuntoglu et al.
MEASUREMENT (2019)
Relevance vector machine for tool wear prediction
Dongdong Kong et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling
Weijian Li et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions
Zhicong Chen et al.
ENERGY CONVERSION AND MANAGEMENT (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)
Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model
Yang Hui et al.
SHOCK AND VIBRATION (2019)
A generic tool wear model and its application to force modeling and wear monitoring in high speed milling
Kunpeng Zhu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Deep learning and its applications to machine health monitoring
Rui Zhao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning
Zhixiong Li et al.
JOURNAL OF MANUFACTURING PROCESSES (2019)
Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model
Kamran Javed et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2018)
Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications
Yanhui Lin et al.
APPLIED SOFT COMPUTING (2018)
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
Rui Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process
Fatemeh Aghazadeh et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (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)
Tool wear monitoring based on kernel principal component analysis and v-support vector regression
Dongdong Kong et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)
Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models
Dongdong Kong et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2017)
Tool condition monitoring in interrupted cutting with acceleration sensors
Juho Ratava et al.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2017)
Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing
Jinjiang Wang et al.
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING (2017)
Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling
Alvar Arnaiz-Gonzalez et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2016)
Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components
Andres Bustillo et al.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING (2016)
Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites
A. I. Azmi
ADVANCES IN ENGINEERING SOFTWARE (2015)
Health assessment and life prediction of cutting tools based on support vector regression
T. Benkedjouh et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2015)
Force based tool wear monitoring system for milling process based on relevance vector machine
Guofeng Wang et al.
ADVANCES IN ENGINEERING SOFTWARE (2014)
Real Time Diagnosis Charts of Thread Quality in Tapping Operations
Alain Gil Del Val et al.
ADVANCES IN MATERIALS PROCESSING TECHNOLOGIES-MESIC V (2014)
On line diagnosis strategy of thread quality in tapping
A. Gil Del Val et al.
MANUFACTURING ENGINEERING SOCIETY INTERNATIONAL CONFERENCE, (MESIC 2013) (2013)
Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool
M. Elangovan et al.
EXPERT SYSTEMS WITH APPLICATIONS (2011)
Multi-category micro-milling tool wear monitoring with continuous hidden Markov models
Kunpeng Zhu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2009)