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。WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
Tianfu Li et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2022)
A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis
Jia Luo et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2021)
Development of a speed invariant deep learning model with application to condition monitoring of rotating machinery
Wo Jae Lee et al.
JOURNAL OF INTELLIGENT MANUFACTURING (2021)
Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions
Qi Li et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2021)
Multireceptive Field Graph Convolutional Networks for Machine Fault Diagnosis
Tianfu Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)
Hybrid physics-informed neural networks for main bearing fatigue prognosis with visual grease inspection
Yigit A. Yucesan et al.
COMPUTERS IN INDUSTRY (2021)
Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach
Zhenghua Chen et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2021)
A transfer convolutional neural network for fault diagnosis based on ResNet-50
Long Wen et al.
NEURAL COMPUTING & APPLICATIONS (2020)
Performance degradation assessment of rolling bearing based on convolutional neural network and deep long-short term memory network
Zheng Wang et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)
A new bearing fault diagnosis method based on modified convolutional neural networks
Jiangquan Zhang et al.
CHINESE JOURNAL OF AERONAUTICS (2020)
Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions
Alexandre Mauricio et al.
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME (2020)
Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
Shao Haidong et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Remaining useful lifetime prediction via deep domain adaptation
Paulo Roberto de Oliveira da Costa et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2020)
An Integrated Approach to Rotating Machinery Fault Diagnosis Using, EEMD, SVM, and Augmented Data
Thiago H. G. Lobato et al.
JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES (2020)
Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples
He Zhiyi et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Automatic alarm setup using extreme value theory
Daniela Toshkova et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery
Biao Wang et al.
NEUROCOMPUTING (2020)
Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty
Weiwen Peng et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)
Simultaneous Bearing Fault Recognition and Remaining Useful Life Prediction Using Joint-Loss Convolutional Neural Network
Ruonan Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi-object deep CNN
Yu Xin et al.
IET SCIENCE MEASUREMENT & TECHNOLOGY (2020)
Fault Detection with Data Imbalance Conditions Based on the Improved Bilayer Convolutional Neural Network
Jing Wang et al.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2020)
Rolling element bearing fault diagnosis for rotating machinery using vibration spectrum imaging and convolutional neural networks
Abdelraouf Youcef Khodja et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2020)
Machinery fault diagnosis with imbalanced data using deep generative adversarial networks
Wei Zhang et al.
MEASUREMENT (2020)
Normalisation of the amplitude modulation caused by time-varying operating conditions for condition monitoring
Stephan Schmidt et al.
MEASUREMENT (2020)
Rolling bearing performance degradation assessment based on improved convolutional neural network with anti-interference
Shaojiang Dong et al.
MEASUREMENT (2020)
Fault detection on fluid machinery using Hidden Markov Models
P. Arpaia et al.
MEASUREMENT (2020)
Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery
Xiaoli Zhao et al.
MEASUREMENT (2020)
Bearing performance degradation assessment based on information-theoretic metric learning and fuzzy c-means clustering
Qingjun Tong et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2020)
Deep learning-based cross-domain adaptation for gearbox fault diagnosis under variable speed conditions
Jaskaran Singh et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2020)
Rolling bearing fault diagnosis based on intelligent optimized self-adaptive deep belief network
Shuzhi Gao et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2020)
Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life
Jehn-Ruey Jiang et al.
SENSORS (2020)
A New Intelligent Bearing Fault Diagnosis Method Using SDP Representation and SE-CNN
Hui Wang et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning
Wentao Mao et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
A Multi-Dimension End-to-End CNN Model for Rotating Devices Fault Diagnosis on High-Speed Train Bogie
Linlin Kou et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)
Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
Guoqiang Li et al.
APPLIED SCIENCES-BASEL (2020)
Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model
Yingjun Deng et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2020)
Automatic Anomaly Detection on In-Production Manufacturing Machines Using Statistical Learning Methods
Federico Pittino et al.
SENSORS (2020)
A sparse auto-encoder method based on compressed sensing and wavelet packet energy entropy for rolling bearing intelligent fault diagnosis
Peiming Shi et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2020)
Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment
Xiaoxi Ding et al.
SHOCK AND VIBRATION (2020)
Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network
Yan Du et al.
SHOCK AND VIBRATION (2020)
Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment
Yuyan Zhang et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Wasserstein Generative Adversarial Network and Convolutional Neural Network (WG-CNN) for Bearing Fault Diagnosis
Hang Yin et al.
MATHEMATICAL PROBLEMS IN ENGINEERING (2020)
A new data-driven transferable remaining useful life prediction approach for bearing under different working conditions
Jun Zhu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Applications of machine learning to machine fault diagnosis: A review and roadmap
Yaguo Lei et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Bearing defect size assessment using wavelet transform based Deep Convolutional Neural Network (DCNN)
Anil Kumar et al.
ALEXANDRIA ENGINEERING JOURNAL (2020)
Potential, challenges and future directions for deep learning in prognostics and health management applications
Olga Fink et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)
Adaptive Channel Weighted CNN With Multisensor Fusion for Condition Monitoring of Helicopter Transmission System
Tianfu Li et al.
IEEE SENSORS JOURNAL (2020)
Multibranch and Multiscale CNN for Fault Diagnosis of Wheelset Bearings Under Strong Noise and Variable Load Condition
Dandan Peng et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions
Ruonan Liu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Roller Bearing Degradation Assessment Based on a Deep MLP Convolution Neural Network Considering Outlier Regions
Dingcheng Zhang et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
DCNN-Based Multi-Signal Induction Motor Fault Diagnosis
Siyu Shao et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2020)
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Xiang Li et al.
KNOWLEDGE-BASED SYSTEMS (2020)
Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions
Bo Zhao et al.
KNOWLEDGE-BASED SYSTEMS (2020)
A deep bi-directional long short-term memory model for automatic rotating speed extraction from raw vibration signals
Meng Rao et al.
MEASUREMENT (2020)
Data synthesis using dual discriminator conditional generative adversarial networks for imbalanced fault diagnosis of rolling bearings
Taisheng Zheng et al.
MEASUREMENT (2020)
A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis
Chenyu Liu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks
Zhuyun Chen et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)
Transfer learning for remaining useful life prediction based on consensus self-organizing models
Yuantao Fan et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2020)
Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels
Yuanhong Chang et al.
RENEWABLE ENERGY (2020)
A hybrid DBN-SOM-PF-based prognostic approach of remaining useful life for wind turbine gearbox
Yubin Pan et al.
RENEWABLE ENERGY (2020)
Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization
Sheng Guo et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2020)
Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study
Zhibin Zhao et al.
ISA TRANSACTIONS (2020)
Physics guided neural network for machining tool wear prediction
Jinjiang Wang et al.
JOURNAL OF MANUFACTURING SYSTEMS (2020)
An unsupervised and enhanced deep belief network for bearing performance degradation assessment
Fan Xu et al.
MEASUREMENT (2020)
A novel method based on nonlinear auto-regression neural network and convolutional neural network for imbalanced fault diagnosis of rotating machinery
Quan Zhou et al.
MEASUREMENT (2020)
Fault-Attention Generative Probabilistic Adversarial Autoencoder for Machine Anomaly Detection
Jingyao Wu et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)
Few-shot transfer learning for intelligent fault diagnosis of machine
Jingyao Wu et al.
MEASUREMENT (2020)
A Robust Performance Degradation Modeling Approach Based on Students t-HMM and Nuisance Attribute Projection
Huiming Jiang et al.
IEEE ACCESS (2020)
Condition Monitoring Techniques of Ball Bearings in Non-stationary Conditions
Matteo Strozzi et al.
DESIGN TOOLS AND METHODS IN INDUSTRIAL ENGINEERING, ADM 2019 (2020)
Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery
Shengnan Tang et al.
IEEE ACCESS (2020)
Bearing Intelligent Fault Diagnosis in the Industrial Internet of Things Context: A Lightweight Convolutional Neural Network
Yanxin Wang et al.
IEEE ACCESS (2020)
An Intelligent Deep Feature Learning Method With Improved Activation Functions for Machine Fault Diagnosis
Wei You et al.
IEEE ACCESS (2020)
An Intelligent Fault Diagnosis Method of Rolling Bearing Under Variable Working Loads Using 1-D Stacked Dilated Convolutional Neural Network
Chao Zhang et al.
IEEE ACCESS (2020)
An Adaptive Anti-Noise Neural Network for Bearing Fault Diagnosis Under Noise and Varying Load Conditions
Guoqiang Jin et al.
IEEE ACCESS (2020)
Deep Cost Adaptive Convolutional Network: A Classification Method for Imbalanced Mechanical Data
Xun Dong et al.
IEEE ACCESS (2020)
A Novel Intelligent Fault Diagnosis Method Based on Variational Mode Decomposition and Ensemble Deep Belief Network
Chao Zhang et al.
IEEE ACCESS (2020)
DCNN for condition monitoring and fault detection in rotating machines and its contribution to the understanding of machine nature
Ana Gonzalez-Muniz et al.
HELIYON (2020)
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
Guoqian Jiang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Enhanced Sparse Period-Group Lasso for Bearing Fault Diagnosis
Zhibin Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network
Jun Zhu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Analysis of Permanent Magnet Synchronous Motor Fault Diagnosis Based on Learning
I-Hsi Kao et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2019)
A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier
Levent Eren et al.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (2019)
Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning
Siyu Shao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks
Yiwei Cheng et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
A Two-Stage Approach for the Remaining Useful Life Prediction of Bearings Using Deep Neural Networks
Min Xia et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Planetary gearbox fault feature learning using conditional variational neural networks under noise environment
You-ren Wang et al.
KNOWLEDGE-BASED SYSTEMS (2019)
Remaining Useful Life Model and Assessment of Mechanical Products: A Brief Review and a Note on the State Space Model Method
Yawei Hu et al.
CHINESE JOURNAL OF MECHANICAL ENGINEERING (2019)
A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment
Zhe Li et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)
A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network
Yuantao Yang et al.
ISA TRANSACTIONS (2019)
A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults
Te Han et al.
KNOWLEDGE-BASED SYSTEMS (2019)
Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis
Teng Wang et al.
MEASUREMENT (2019)
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
Andre Listou Ellefsen et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2019)
Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction
Xiang Li et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2019)
Fault Diagnosis of Rotating Machinery under Noisy Environment Conditions Based on a 1-D Convolutional Autoencoder and 1-D Convolutional Neural Network
Xingchen Liu et al.
SENSORS (2019)
Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning
Gaowei Xu et al.
SENSORS (2019)
A Bearing Performance Degradation Modeling Method Based on EMD-SVD and Fuzzy Neural Network
Jingbo Gai et al.
SHOCK AND VIBRATION (2019)
Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network
M. M. Manjurul Islam et al.
COMPUTERS IN INDUSTRY (2019)
Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform
Renxiang Chen et al.
COMPUTERS IN INDUSTRY (2019)
Generative adversarial networks for data augmentation in machine fault diagnosis
Siyu Shao et al.
COMPUTERS IN INDUSTRY (2019)
Deep convolutional neural network based planet bearing fault classification
Dezun Zhao et al.
COMPUTERS IN INDUSTRY (2019)
An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes
Yan Han et al.
COMPUTERS IN INDUSTRY (2019)
Bearing performance degradation assessment using long short-term memory recurrent network
Bin Zhang et al.
COMPUTERS IN INDUSTRY (2019)
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
Chuang Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
A Data-Driven Monitoring Scheme for Rotating Machinery Via Self-Comparison Approach
Ming Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis
Zhibin Zhao et al.
JOURNAL OF SOUND AND VIBRATION (2019)
A novel transfer learning method for robust fault diagnosis of rotating machines under variable working conditions
Weiwei Qian et al.
MEASUREMENT (2019)
Fault diagnosis of rolling bearing under fluctuating speed and variable load based on TCO Spectrum and Stacking Auto-encoder
Zhou Xiang et al.
MEASUREMENT (2019)
NOSCNN: A robust method for fault diagnosis of RV reducer
Peng Peng et al.
MEASUREMENT (2019)
Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions
Md Junayed Hasan et al.
MEASUREMENT (2019)
A novel gas turbine fault diagnosis method based on transfer learning with CNN
Shi-sheng Zhong 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)
A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes
Huipeng Chen et al.
MEASUREMENT (2019)
A novel convolutional transfer feature discrimination network for unbalanced fault diagnosis under variable rotational speeds
Kun Xu et al.
MEASUREMENT SCIENCE AND TECHNOLOGY (2019)
Adaptive event-triggered anomaly detection in compressed vibration data
Yang Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion
Wenfeng Gong et al.
SENSORS (2019)
Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals
Xueyi Li et al.
APPLIED SCIENCES-BASEL (2019)
Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network
Chunzhi Wu et al.
COMPUTERS IN INDUSTRY (2019)
A multimodal and hybrid deep neural network model for Remaining Useful Life estimation
Ali Al-Dulaimi et al.
COMPUTERS IN INDUSTRY (2019)
Real-time broken rotor bar fault detection and classification by shallow 1D convolutional neural networks
Turker Ince
ELECTRICAL ENGINEERING (2019)
A Comprehensive Survey of Prognostics and Health Management Based on Deep Learning for Autonomous Ships
Andre Listou Ellefsen et al.
IEEE TRANSACTIONS ON RELIABILITY (2019)
CEEMD-assisted bearing degradation assessment using tight clustering
Yanfei Lu et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY (2019)
Failure Detection and Remaining Life Estimation for Ion Mill Etching Process Through Deep-Learning Based Multimodal Data Fusion
Anqi He et al.
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME (2019)
A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion
Qibin Wang et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2019)
Vibration signal based condition monitoring of mechanical equipment with scattering transform
P. S. Ambika et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2019)
Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion
Yubin Pan et al.
MECHANISM AND MACHINE THEORY (2019)
Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform
Guoqiang Li et al.
SENSORS (2019)
A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
Wenkai Liu et al.
SENSORS (2019)
Application of Multi-Dimension Input Convolutional Neural Network in Fault Diagnosis of Rolling Bearings
Tao Zan et al.
APPLIED SCIENCES-BASEL (2019)
Fault Diagnosis of Induction Motor Using Convolutional Neural Network
Jong-Hyun Lee et al.
APPLIED SCIENCES-BASEL (2019)
PARTICLE SWARM-OPTIMIZED SUPPORT VECTOR MACHINES AND PRE-PROCESSING TECHNIQUES FOR REMAINING USEFUL LIFE ESTIMATION OF BEARINGS
Caio Bezerra Souto Maior et al.
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY (2019)
Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems
Roozbeh Razavi-Far et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Joint Learning of Degradation Assessment and RUL Prediction for Aeroengines via Dual-Task Deep LSTM Networks
Huihui Miao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Bearing degradation assessment based on entropy with time parameter and fuzzy c-means clustering
Guozeng Liu et al.
JOURNAL OF VIBROENGINEERING (2019)
Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme
Wennian Yu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Generalization of deep neural network for bearing fault diagnosis under different working conditions using multiple kernel method
Zenghui An et al.
NEUROCOMPUTING (2019)
Fault Diagnosis System for Induction Motors by CNN Using Empirical Wavelet Transform
Yu-Min Hsueh et al.
SYMMETRY-BASEL (2019)
Combining multiple deep learning algorithms for prognostic and health management of aircraft
Changchang Che et al.
AEROSPACE SCIENCE AND TECHNOLOGY (2019)
Bearing Fault Diagnosis Based on Shallow Multi-Scale Convolutional Neural Network with Attention
Tengda Huang et al.
ENERGIES (2019)
Data-driven approach augmented in simulation for robust fault prognosis
M. A. Djeziri et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)
A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions
Cheng-Geng Huang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
A deep learning driven method for fault classification and degradation assessment in mechanical equipment
Zhe Li et al.
COMPUTERS IN INDUSTRY (2019)
Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform
Pengfei Liang et al.
COMPUTERS IN INDUSTRY (2019)
A hybrid ARIMA-SVM model for the study of the remaining useful life of aircraft engines
Celestino Ordonez et al.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS (2019)
Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture
Boyuan Yang et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)
Deep learning and its applications to machine health monitoring
Rui Zhao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2019)
Bidirectional handshaking LSTM for remaining useful life prediction
Ahmed Elsheikh et al.
NEUROCOMPUTING (2019)
Exploiting the generative adversarial framework for one-class multi-dimensional fault detection
Spyridon Plakias et al.
NEUROCOMPUTING (2019)
Deep transfer learning for rolling bearing fault diagnosis under variable operating conditions
Changchang Che et al.
ADVANCES IN MECHANICAL ENGINEERING (2019)
A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction
Jialin Li et al.
IEEE ACCESS (2019)
Imbalanced Fault Diagnosis of Rolling Bearing Based on Generative Adversarial Network: A Comparative Study
Wentao Mao et al.
IEEE ACCESS (2019)
A Novel Deeper One-Dimensional CNN With Residual Learning for Fault Diagnosis of Wheelset Bearings in High-Speed Trains
Dandan Peng et al.
IEEE ACCESS (2019)
Validation of Data-Driven Labeling Approaches Using a Novel Deep Network Structure for Remaining Useful Life Predictions
Andre Listou Ellefsen et al.
IEEE ACCESS (2019)
Remaining Useful Life Prediction Using Time-Frequency Feature and Multiple Recurrent Neural Networks
Jihoon Hong et al.
2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) (2019)
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Dan Li et al.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: TEXT AND TIME SERIES, PT IV (2019)
Condition Monitoring of Wind Turbine Gearbox Bearing Based on Deep Learning Model
Jian Fu et al.
IEEE ACCESS (2019)
A Domain Adaptive Convolutional LSTM Model for Prognostic Remaining Useful Life Estimation under Variant Conditions
Shuyang Yu et al.
2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-PARIS) (2019)
Degradation Assessment of Bearings Using Deep Convolutional Inner-Ensemble Learning with Outlier Removal
Dingcheng Zhang et al.
2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-PARIS) (2019)
Health indicator construction of rolling bearings based on deep convolutional neural network considering phase degradation
Daoming She et al.
2019 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-PARIS) (2019)
Edge-Based RNN Anomaly Detection Platform in Machine Tools
Chia-Yu Lin et al.
SMART SCIENCE (2019)
Improved Deep Transfer Auto-Encoder for Fault Diagnosis of Gearbox Under Variable Working Conditions With Small Training Samples
Zhiyi He et al.
IEEE ACCESS (2019)
A Fault Diagnostic Method for Induction Motors Based on Feature Incremental Broad Learning and Singular Value Decomposition
Sai Biao Jiang et al.
IEEE ACCESS (2019)
Rolling Bearing Initial Fault Detection Using Long Short-Term Memory Recurrent Network
Huaitao Shi et al.
IEEE ACCESS (2019)
A Deep Learning Method for Bearing Fault Diagnosis Based on Time-frequency Image
Jianyu Wang et al.
IEEE ACCESS (2019)
AESGRU: An Attention-Based Temporal Correlation Approach for End-to-End Machine Health Perception
Weiting Zhang et al.
IEEE ACCESS (2019)
A New Deep Fusion Network for Automatic Mechanical Fault Feature Learning
Yumei Qi et al.
IEEE ACCESS (2019)
An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions
Huihui Qiao et al.
IEEE ACCESS (2019)
Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression
Akhand Rai et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE (2018)
Rolling bearing fault detection using continuous deep belief network with locally linear embedding
Haidong Shao et al.
COMPUTERS IN INDUSTRY (2018)
Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods
Zhengxin Zhang et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2018)
A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
Long Wen et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks
Rui Zhao et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)
Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data
Meng Ma et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2018)
A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery
Hongkai Jiang et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2018)
An intelligent fault diagnosis approach for planetary gearboxes based on deep belief networks and uniformed features
Xin Wang et al.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (2018)
Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning
Yuyan Zhang et al.
JOURNAL OF MANUFACTURING SYSTEMS (2018)
Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization
Feng Jia et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Graph-based structural change detection for rotating machinery monitoring
Guoliang Lu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Artificial intelligence for fault diagnosis of rotating machinery: A review
Ruonan Liu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
A review on the application of deep learning in system health management
Samir Khan et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
Wei Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Yaguo Lei et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing
Haidong Shao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)
An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition
Zirui Wang et al.
NEUROCOMPUTING (2018)
A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning
Xiang Li et al.
NEUROCOMPUTING (2018)
Remaining useful life estimation in prognostics using deep convolution neural networks
Xiang Li et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2018)
Using Deep Learning-Based Approach to Predict Remaining Useful Life of Rotating Components
Jason Deutsch et al.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2018)
A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network
Youngji Yoo et al.
APPLIED SCIENCES-BASEL (2018)
An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive
Changqing Shen et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2018)
Prediction of flow blockages and impending cavitation in centrifugal pumps using Support Vector Machine (SVM) algorithms based on vibration measurements
Asish Kumar Panda et al.
MEASUREMENT (2018)
A prognostic methodology for power MOSFETs under thermal stress using echo state network and particle filter
Z. Li et al.
MICROELECTRONICS RELIABILITY (2018)
Developing ladder network for intelligent evaluation system: Case of remaining useful life prediction for centrifugal pumps
Rui He et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2018)
K-PdM: KPI-Oriented Machinery Deterioration Estimation Framework for Predictive Maintenance Using Cluster-Based Hidden Markov Model
Zhenyu Wu et al.
IEEE ACCESS (2018)
Deep convolutional neural networks for Bearings failure predictionand temperature correlation
D. Belmiloud et al.
JOURNAL OF VIBROENGINEERING (2018)
An Intelligent Fault Diagnosis Method for Bearings with Variable Rotating Speed Based on Pythagorean Spatial Pyramid Pooling CNN
Sheng Guo et al.
SENSORS (2018)
Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
Ansi Zhang et al.
APPLIED SCIENCES-BASEL (2018)
Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network
Wentao Mao et al.
ADVANCES IN MECHANICAL ENGINEERING (2018)
Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering
Bing Wang et al.
ADVANCES IN MECHANICAL ENGINEERING (2018)
Data-Driven Fault Prognosis Based on Incomplete Time Slice Dynamic Bayesian Network
Zhengdao Zhang et al.
IFAC PAPERSONLINE (2018)
An intelligent performance degradation assessment method for bearings
Huiming Jiang et al.
JOURNAL OF VIBRATION AND CONTROL (2017)
Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery
Xiaojie Guo et al.
APPLIED SCIENCES-BASEL (2017)
Online feature learning for condition monitoring of rotating machinery
S. Martin-del-Campo et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2017)
Deep Model Based Domain Adaptation for Fault Diagnosis
Weining Lu et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2017)
An Integrated Class-Imbalanced Learning Scheme for Diagnosing Bearing Defects in Induction Motors
Roozbeh Razavi-Far et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2017)
Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data
Taehoon Ko et al.
INDUSTRIAL MANAGEMENT & DATA SYSTEMS (2017)
Application of selected method of anomaly detection in signals acquired during welding process monitoring
Anna Bzymek
INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY (2017)
On the combination of support vector machines and segmentation algorithms for anomaly detection: A petroleum industry comparative study
Luis Marti et al.
JOURNAL OF APPLIED LOGIC (2017)
Multi-bearing remaining useful life collaborative prediction: A deep learning approach
Lei Ren et al.
JOURNAL OF MANUFACTURING SYSTEMS (2017)
An effective health indicator based on two dimensional hidden Markov model
Lin Li et al.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2017)
Performance Degradation Assessment of Rolling Element Bearings using Improved Fuzzy Entropy
Keheng Zhu et al.
MEASUREMENT SCIENCE REVIEW (2017)
A novel framework of change-point detection for machine monitoring
Guoliang Lu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine
Wentao Mao et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump
Ming Zhang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering
Akhand Rai et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)
Unsupervised real-time anomaly detection for streaming data
Subutai Ahmad et al.
NEUROCOMPUTING (2017)
Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM
Ke Yan et al.
NEUROCOMPUTING (2017)
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Rui Zhao et al.
SENSORS (2017)
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
Wei Zhang et al.
SENSORS (2017)
Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification
Chen Lu et al.
SIGNAL PROCESSING (2017)
A new fault diagnosis method based on deep belief network and support vector machine with Teager-Kaiser energy operator for bearings
Dongying Han et al.
ADVANCES IN MECHANICAL ENGINEERING (2017)
A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery
Wei You et al.
ADVANCES IN MECHANICAL ENGINEERING (2017)
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning
Jun Inoue et al.
2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2017) (2017)
Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis
Guoqian Jiang et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2017)
Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition
Yujie Cheng et al.
MATERIALS (2017)
Rolling Element Bearings Fault Intelligent Diagnosis Based on Convolutional Neural Networks Using Raw Sensing Signal
Wei Zhang et al.
ADVANCES IN INTELLIGENT INFORMATION HIDING AND MULTIMEDIA SIGNAL PROCESSING, VOL 2 (2017)
Matching Synchrosqueezing Wavelet Transform and Application to Aeroengine Vibration Monitoring
Shibin Wang et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2017)
An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm
Guannan Li et al.
ENERGY AND BUILDINGS (2016)
Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings
Meng Gan et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2016)
Joint probability distributions of correlation coefficients in the diagnostics of mobile work machines
Tomi Krogerus et al.
MECHATRONICS (2016)
Vibration-Based Adaptive Novelty Detection Method for Monitoring Faults in a Kinematic Chain
Jesus Adolfo Carino-Corrales et al.
SHOCK AND VIBRATION (2016)
Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD
Jianmin Zhou et al.
SHOCK AND VIBRATION (2016)
Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals
Hongmei Liu et al.
SHOCK AND VIBRATION (2016)
BEARING PERFORMANCE DEGRADATION ASSESSMENT BY ORTHOGONAL LOCAL PRESERVING PROJECTION AND CONTINUOUS HIDDEN MARKOV MODEL
Tao Liu et al.
TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING (2016)
Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction
Shankar Sankararaman
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2015)
Anomaly Detection Based on Sensor Data in Petroleum Industry Applications
Luis Marti et al.
SENSORS (2015)
Prognostics and health management design for rotary machinery systems-Reviews, methodology and applications
Jay Lee et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2014)
Wind turbine condition monitoring by the approach of SCADA data analysis
Wenxian Yang et al.
RENEWABLE ENERGY (2013)
A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models
Diego Alejandro Tobon-Mejia et al.
IEEE TRANSACTIONS ON RELIABILITY (2012)
A nonparametric fault isolation approach through one-class classification algorithms
Seoung Bum Kim et al.
IIE TRANSACTIONS (2011)
Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection
Meik Schlechtingen et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2011)
Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system
Alexandre Muller et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2008)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
分享论文
