A hybrid deep-learning model for fault diagnosis of rolling bearings

Domain adaptive deep belief network for rolling bearing fault diagnosis

(2020) Changchang Che et al.   COMPUTERS & INDUSTRIAL ENGINEERING

A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines

(2020) Fushun Liu et al.   MARINE STRUCTURES

Multisensor bearing fault diagnosis based on one-dimensional convolutional long short-term memory networks

(2020) Shijie Hao et al.   MEASUREMENT

Fault detection and identification of rolling element bearings with Attentive Dense CNN

(2020) Spyridon Plakias et al.   NEUROCOMPUTING

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning

(2019) Gaowei Xu et al.   SENSORS

Planetary gear fault diagnosis using stacked denoising autoencoder and gated recurrent unit neural network under noisy environment and time-varying rotational speed conditions

(2019) Jun Yu et al.   MEASUREMENT SCIENCE and TECHNOLOGY

A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion

(2019) Gong et al.   SENSORS

Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network

(2019) Chunzhi Wu et al.   COMPUTERS IN INDUSTRY

In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning

(2019) Vigneashwara Pandiyan et al.   ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

(2019) Adam Glowacz et al.   Measurement Science Review

Acoustic fault analysis of three commutator motors

(2019) Adam Glowacz   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier

(2019) Yaakoub Boualleg et al.   IEEE Geoscience and Remote Sensing Letters

A multi-ensemble method based on deep auto-encoders for fault diagnosis of rolling bearings

(2019) Xianguang Kong et al.   MEASUREMENT

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

(2018) Tom Young et al.   IEEE Computational Intelligence Magazine

A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

(2018) Long Wen et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier

(2018) Levent Eren et al.   Journal of Signal Processing Systems for Signal Image and Video Technology

Artificial intelligence for fault diagnosis of rotating machinery: A review

(2018) Ruonan Liu et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

(2018) Haidong Shao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load

(2018) Wei Zhang et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

DCNR: deep cube CNN with random forest for hyperspectral image classification

(2018) Tao Li et al.   MULTIMEDIA TOOLS AND APPLICATIONS

Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations

(2018) Zhixiong Li et al.   RENEWABLE ENERGY

Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

(2018) Naveed Akhtar et al.   IEEE Access

Frequency and time fault diagnosis methods of power transformers

(2018) Miroslav Gutten et al.   Measurement Science Review

Deep learning and its applications to machine health monitoring

(2018) Rui Zhao et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Parsimonious Network Based on a Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis

(2018) Wahyu Caesarendra et al.   Applied Sciences-Basel

Fault diagnosis of single-phase induction motor based on acoustic signals

(2018) Adam Glowacz   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

(2016) Meng Gan et al.   MECHANICAL SYSTEMS AND SIGNAL PROCESSING

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

(2016) Chao Zhang et al.   PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE

A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings

(2016) Akhand Rai et al.   TRIBOLOGY INTERNATIONAL

Rolling bearing fault diagnosis using an optimization deep belief network

(2015) Haidong Shao et al.   MEASUREMENT SCIENCE and TECHNOLOGY

Deep learning

(2015) Yann LeCun et al.   NATURE

Fault diagnosis of ball bearings using continuous wavelet transform

(2010) P.K. Kankar et al.   APPLIED SOFT COMPUTING