A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336

Advances in Neural Information Processing Systems 19

(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043

Caffe: Convolutional Architecture for Fast Feature Embedding

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,330

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,050

Deep Learning: Methods and Applications

Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.3115/v1/p15-1150 · Cited by 3,081

A Practical Guide to Training Restricted Boltzmann Machines

Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852

Deep Boltzmann machines

Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890

Deep Learning: Methods and Applications

Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,789

Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2015.10.025 · Cited by 1,710

Building high-level features using large scale unsupervised learning

Le, Q. V. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639343 · Cited by 1,695

Wavelets for fault diagnosis of rotary machines: A review with applications

Yan, R., Gao, R. X., & Chen, X. (2013) · Signal Processing · doi:10.1016/j.sigpro.2013.04.015 · Cited by 1,451

New types of deep neural network learning for speech recognition and related applications: an overview

Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,259

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,252

An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data

Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191

Learning multiple layers of representation

Hinton, G. E. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.09.004 · Cited by 1,050

Dynamic Memory Networks for Visual and Textual Question AnsweringPreprint

Xiong, C., Merity, S., & Socher, R. (2016) · International Conference on Machine Learning · doi:10.48550/arxiv.1603.01417 · Cited by 770

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., & Chen, X. (2016) · Measurement · doi:10.1016/j.measurement.2016.04.007 · Cited by 715

Failure diagnosis using deep belief learning based health state classification

Tamilselvan, P., & Wang, P. (2013) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2013.02.022 · Cited by 631

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Riera‐Guasp, M., Daviu, J. A. A., & Capolino, G. (2014) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2375853 · Cited by 580

An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks

Tran, V. T., Althobiani, F., & Ball, A. D. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.026 · Cited by 386

Time-Frequency Signal Analysis and Processing

Boashash, B. (2016) · Elsevier eBooks · doi:10.1016/c2012-0-00024-5 · Cited by 376

Building high-level features using large scale unsupervised learningPreprint

Le, Q. V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G. S., Dean, J., & Ng, A. Y. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1112.6209 · Cited by 341

To recognize shapes, first learn to generate images

Hinton, G. E. (2007) · Progress in brain research · doi:10.1016/s0079-6123(06)65034-6 · Cited by 307

Stator Fault Diagnostics in Squirrel Cage Three-Phase Induction Motor Drives Using the Instantaneous Active and Reactive Power Signature Analyses

Drif, M., & Cardoso, A. J. M. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2307013 · Cited by 238

Investigation of full-sequence training of deep belief networks for speech recognition

Mohamed, A., Yu, D., & Deng, L. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-304 · Cited by 213

Model-based fault diagnosis in electric drives using machine learning

Murphey, Y. L., Masrur, M. A., Chen, Z., & Zhang, B. (2006) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2006.875568 · Cited by 170

Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization

Gao, H., Liang, L., Chen, X., & Xu, G. (2014) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2014.1103.166 · Cited by 146

Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms

Chen, C., Zhang, B., & Vachtsevanos, G. (2011) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2011.2169182 · Cited by 136

Recursive Undecimated Wavelet Packet Transform and DAG SVM for Induction Motor Diagnosis

Keskes, H., & Braham, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2462315 · Cited by 121

Statistical and Neural-Network Approaches for the Classification of Induction Machine Faults Using the Ambiguity Plane Representation

Boukra, T., Lebaroud, A., & Clerc, G. (2012) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2012.2216242 · Cited by 88

EMD-Based Analysis of Industrial Induction Motors With Broken Rotor Bars for Identification of Operating Point at Different Supply Modes

Faiz, J., Ghorbanian, V., & Ebrahimi, B. M. (2013) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2013.2289941 · Cited by 82

Support vector machine classifier for diagnosis in electrical machines: Application to broken bar

Matić, D., Kulić, F., Pineda‐Sanchez, M., & Kamenko, I. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.214 · Cited by 79

Scale Invariant Feature Extraction Algorithm for the Automatic Diagnosis of Rotor Asymmetries in Induction Motors

Daviu, J. A. A., Aviyente, S., Strangas, E. G., & Riera‐Guasp, M. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2198659 · Cited by 77

Greedy Layer-Wise Training of Deep Networks

Schölkopf, B., Platt, J. C., & Hofmann, T. F. (2007) · Cited by 60

A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors

Karvelis, P. S., Georgoulas, G., Tsoumas, I. P., Daviu, J. A. A., Climente-Alarcón, V., & Stylios, C. D. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2463680 · Cited by 57

Structural health monitoring by using a sparse coding-based deep learning algorithm with wireless sensor networks

Guo, J., Xie, X., Bie, R., & Sun, L. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0800-5 · Cited by 55

Multi-scale enveloping order spectrogram for rotating machine health diagnosis

Wang, J., Gao, R. X., & Yan, R. (2013) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2013.06.001 · Cited by 51

Automated fault detection using deep belief networks for the quality inspection of electromotors

Sun, J., Wyss, R., Steinecker, A., & Glocker, P. (2014) · tm - Technisches Messen · doi:10.1515/teme-2014-1006 · Cited by 47

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

Zhang, M., Tang, J., Zhang, X., & Zhang, J. (2016) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2015.1214.147 · Cited by 35

Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

Wang, Y., Zhang, F., Cui, T., & Zhou, J. (2016) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2015.1211.145 · Cited by 35

Improved CICA algorithm used for single channel compound fault diagnosis of rolling bearings

Chen, G., Qie, L., Zhang, A., & Han, J. (2015) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2015.1026.127 · Cited by 26

Induction motor fault diagnosis using multiple class feature selection

Yang, X., Yan, R., & Gao, R. X. (2015) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2015.7151275 · Cited by 19

Application of Deep Belief Networks for Precision Mechanism Quality Inspection

Sun, J., Steinecker, A., & Glocker, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-45586-9_12 · Cited by 9

Vehicle detection based on visual saliency and deep sparse convolution hierarchical model

Cai, Y., Wang, H., Chen, X., Gao, L. F., & Chen, L. (2016) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2016.0408.048 · Cited by 8

Improved CICA Algorithm Used for Single Channel Compound Fault Diagnosis of Rolling Bearings

Chen, Guohua, Qie, Long-fei, Zhang, Ai-jun, ., H., & Jin, J. (2016) · 中国机械工程学报：英文版 · Cited by 8

Applications of machine learning to machine fault diagnosis: A review and roadmap

Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2019.106587 · Cited by 2,997

Deep learning and its applications to machine health monitoring

Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2864759 · Cited by 1,690

Machine learning and data mining in manufacturing

Doğan, A., & Birant, D. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114060 · Cited by 750

DCNN-Based Multi-Signal Induction Motor Fault Diagnosis

Shao, S., Yan, R., Lu, Y., Wang, P. E., & Gao, R. X. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2925247 · Cited by 445

Deep Belief Networks for Intelligent Fault Diagnosis

Yan, R., & Zhao, Z. (2024) · CRC Press eBooks · doi:10.1201/9781003474463-4

Learning features from vibration signals for induction motor fault diagnosis

Shao, S., Sun, W., Wang, P. E., Gao, R. X., & Yan, R. (2016) · International Symposium on Flexible Automation (ISFA) · doi:10.1109/isfa.2016.7790138 · Cited by 59

Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor

Katta, P., Karunanithi, K., Raja, S. P., Ramesh, S., Prakash, S. V. J., & Joseph, D. (2024) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.31616 · Cited by 5

Multiclass Fault Diagnosis and Novelty Detection of Induction Motor Using Deep Learning Algorithm Based on Frequency Domain Signal

Gupta, A., Tiwari, R., & Bordoloi, D. J. (2023) · Mechanisms and machine science · doi:10.1007/978-981-99-4721-8_8

Regression Based Performance Analysis and Fault Detection in Induction Motors by Using Deep Learning Technique

Katta, P., Karunanithi, K., Raja, S. P., Subramanian, R., & Chandrasekar, P. (2023) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij.28435 · Cited by 1