Health condition monitoring of machines based on long short-term memory convolutional autoencoder

[1]  Mohamed Hammad,et al.  ResNet‐Attention model for human authentication using ECG signals , 2020, Expert Syst. J. Knowl. Eng..

[2]  Michael E. Fitzpatrick,et al.  Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform , 2017, Expert Syst. Appl..

[3]  Jianbo Yu,et al.  A hybrid feature selection scheme and self-organizing map model for machine health assessment , 2011, Appl. Soft Comput..

[4]  Tangbin Xia,et al.  Recent advances in prognostics and health management for advanced manufacturing paradigms , 2018, Reliab. Eng. Syst. Saf..

[5]  Liang Guo,et al.  Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring , 2016 .

[6]  Rik Van de Walle,et al.  Deep Learning for Infrared Thermal Image Based Machine Health Monitoring , 2017, IEEE/ASME Transactions on Mechatronics.

[7]  Bo-Suk Yang,et al.  Application of relevance vector machine and survival probability to machine degradation assessment , 2011, Expert Syst. Appl..

[8]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[9]  Jianbo Yu,et al.  Health Condition Monitoring of Machines Based on Hidden Markov Model and Contribution Analysis , 2012, IEEE Transactions on Instrumentation and Measurement.

[10]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[11]  Wenjing Jin,et al.  Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment , 2016, IEEE Transactions on Industrial Electronics.

[12]  Minping Jia,et al.  Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate , 2019, Measurement.

[13]  David J. Sandoz,et al.  The application of principal component analysis and kernel density estimation to enhance process monitoring , 2000 .

[14]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[15]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[16]  Wei Shi,et al.  Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification , 2016, ACL.

[17]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[18]  Jay Lee,et al.  A similarity based methodology for machine prognostics by using kernel two sample test. , 2020, ISA transactions.

[19]  Brigitte Chebel-Morello,et al.  Direct Remaining Useful Life Estimation Based on Support Vector Regression , 2017, IEEE Transactions on Industrial Electronics.

[20]  U. Rajendra Acharya,et al.  DGHNL: A new deep genetic hierarchical network of learners for prediction of credit scoring , 2020, Inf. Sci..

[21]  Wennian Yu,et al.  Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme , 2019, Mechanical Systems and Signal Processing.

[22]  Sengul Dogan,et al.  Ensemble residual network-based gender and activity recognition method with signals , 2020, The Journal of Supercomputing.

[23]  Nagi Gebraeel,et al.  Multistream sensor fusion-based prognostics model for systems with single failure modes , 2017, Reliab. Eng. Syst. Saf..

[24]  Li Lin,et al.  Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.

[25]  U. Rajendra Acharya,et al.  Application of new deep genetic cascade ensemble of SVM classifiers to predict the Australian credit scoring , 2019, Appl. Soft Comput..

[26]  Liang Guo,et al.  Machinery health indicator construction based on convolutional neural networks considering trend burr , 2018, Neurocomputing.

[27]  Fanrang Kong,et al.  Subspace-based gearbox condition monitoring by kernel principal component analysis , 2007 .

[28]  Shijin Wang,et al.  LSTM Neural Reordering Feature for Statistical Machine Translation , 2015, NAACL.

[29]  Bin Li,et al.  Early Fault Detection of Machine Tools Based on Deep Learning and Dynamic Identification , 2019, IEEE Transactions on Industrial Electronics.

[30]  Kaixiang Peng,et al.  A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter , 2019, Neurocomputing.

[31]  Changchang Che,et al.  Combining multiple deep learning algorithms for prognostic and health management of aircraft , 2019, Aerospace Science and Technology.

[32]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[33]  Jie Chen,et al.  Degradation evaluation of slewing bearing using HMM and improved GRU , 2019, Measurement.

[34]  Kay Chen Tan,et al.  Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[36]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[37]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[40]  Li Lin,et al.  Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network , 2016, 2016 IEEE International Conference on Aircraft Utility Systems (AUS).

[41]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[42]  Xiang Li,et al.  Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..

[43]  Khanh T.P. Nguyen,et al.  A new dynamic predictive maintenance framework using deep learning for failure prognostics , 2019, Reliab. Eng. Syst. Saf..