A novel fusion diagnosis method for rotor system fault based on deep learning and multi-sourced heterogeneous monitoring data

[1]  Hongkai Jiang,et al.  An adaptive deep convolutional neural network for rolling bearing fault diagnosis , 2017 .

[2]  Andrew D. Ball,et al.  An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks , 2014, Expert Syst. Appl..

[3]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[4]  Peng Wang,et al.  An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox , 2017, Sensors.

[5]  Lixiang Duan,et al.  Segmented infrared image analysis for rotating machinery fault diagnosis , 2016 .

[6]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[7]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[8]  ZhiQiang Chen,et al.  Gearbox Fault Identification and Classification with Convolutional Neural Networks , 2015 .

[9]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[10]  Liang Guo,et al.  A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.

[11]  Qingbo He,et al.  Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis , 2017, IEEE Transactions on Instrumentation and Measurement.

[12]  Qi Liu,et al.  A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion , 2017, Microelectron. Reliab..

[13]  T. Jayakumar,et al.  Infrared thermography for condition monitoring – A review , 2013 .

[14]  Haidong Shao,et al.  Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing , 2018 .

[15]  Yan Wang,et al.  An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis , 2018 .

[16]  Edwin Lughofer,et al.  Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations , 2014, Inf. Fusion.

[17]  Bo-Suk Yang,et al.  Intelligent fault diagnosis of rotating machinery using infrared thermal image , 2012, Expert Syst. Appl..

[18]  Xian-Bo Wang,et al.  Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach , 2016 .

[19]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[20]  Sofie Van Hoecke,et al.  Thermal image based fault diagnosis for rotating machinery , 2015 .

[21]  Cécile Barat,et al.  String representations and distances in deep Convolutional Neural Networks for image classification , 2016, Pattern Recognit..

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Fengshou Gu,et al.  Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis , 2013 .

[24]  Diego Cabrera,et al.  Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis , 2015, Neurocomputing.

[25]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[26]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[27]  Lixiang Duan,et al.  NSCT-Based Infrared Image Enhancement Method for Rotating Machinery Fault Diagnosis , 2016, IEEE Transactions on Instrumentation and Measurement.

[28]  Diego Cabrera,et al.  Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals , 2016 .

[29]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[30]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

[31]  Xun Sun,et al.  Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network , 2017 .

[32]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[33]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[34]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[35]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[36]  Robert X. Gao,et al.  Virtualization and deep recognition for system fault classification , 2017 .

[37]  Haidong Shao,et al.  Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .

[38]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.