Deep learning for smart manufacturing: Methods and applications

Journal of Manufacturing Systems - Tập 48 - Trang 144-156 - 2018
Jinjiang Wang1, Yulin Ma1, Laibin Zhang1, Robert X. Gao2, Dazhong Wu3
1School of Mechanical and Transportation Engineering, China University of Petroleum, Beijing 102249, China
2Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
3Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, 32816, USA

Tài liệu tham khảo

Putnik, 2013, Scalability in manufacturing systems design and operation: state-of-the-art and future developments roadmap, CIRP Ann Manuf Technol, 62, 751, 10.1016/j.cirp.2013.05.002 Lee, 2017, A classification scheme for smart manufacturing systems' performance metrics, Smart Sustain Manuf Syst, 1, 52, 10.1520/SSMS20160012 Hu, 2013, Design and application of a real-time industrial Ethernet protocol under Linux using RTAI, Int J Comput Integr Manuf, 26, 429, 10.1080/0951192X.2012.731609 Ye, 2016, Design and development of a CNC machining process knowledge base using cloud technology, Int J Adv Manuf Technol, 1 Tao, 2017, New IT driven service-oriented smart manufacturing: framework and characteristics, IEEE Trans Syst Man Cybern Syst, 99, 1 Ang, 2017, Energy-efficient through-life smart design, manufacturing and operation of ships in an industry 4.0 environment, Energies, 10, 610, 10.3390/en10050610 Huang, 2016, Research and development of industrial real-time Ethernet performance testing system used for CNC system, Int J Adv Manuf Technol, 83, 1199, 10.1007/s00170-015-7625-x Lalanda, 2017, Autonomic mediation middleware for smart manufacturing, IEEE Internet Comput, 21, 32, 10.1109/MIC.2017.18 Smart Manufacturing Coalition, 2013 Wang, 2015, Current status and advancement of cyber-physical systems in manufacturing, J Manuf Syst, 37, 517, 10.1016/j.jmsy.2015.04.008 Wang, 2015, Cloud computing for cloud manufacturing: benefits and limitations, J Manuf Sci Eng, 137, 1, 10.1115/1.4030209 Lu, 2014, Development of a hybrid manufacturing cloud, J Manuf Syst, 33, 551, 10.1016/j.jmsy.2014.05.003 Wu, 2015, Cloud-based design and manufacturing: status and promise, Comput Aided Des, 59, 1, 10.1016/j.cad.2014.07.006 Choudhary, 2009, Data mining in manufacturing: a review based on the kind of knowledge, J Intell Manuf, 20, 501, 10.1007/s10845-008-0145-x Lade, 2017, Manufacturing analytics and industrial internet of things, IEEE Intell Syst, 32, 74, 10.1109/MIS.2017.49 Monostori, 1996, Machine learning approaches to manufacturing, CIRP Ann Manuf Technol, 45, 675, 10.1016/S0007-8506(18)30216-6 Teti, 2010, Advanced monitoring of machining operations, CIRP Ann Manuf Technol, 59, 717, 10.1016/j.cirp.2010.05.010 Helu, 2016, Enabling smart manufacturing technologies for decision-making support, Proceedings of the ASME international design engineering technical conferences and computers and information in engineering conference (IDETC/CIE), 1 Wuest, 2016, Machine learning in manufacturing: advantages, challenges, and applications, Prod Manuf Res, 4, 23 Gao, 2015, Cloud-enabled prognosis for manufacturing, CIRP Ann Manuf Technol, 64, 749, 10.1016/j.cirp.2015.05.011 Wu, 2017, A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests, J Manuf Sci Eng, 139, 1, 10.1115/1.4036350 2017 Mcculloch, 1943, A logical calculus of the ideas immanent in nervous activity, Bull Math Biophys, 5, 115, 10.1007/BF02478259 Samuel, 2010, Some studies in machine learning using the game of checkers II—recent progress, Annu Rev Autom Program, 44, 206 Rosenblatt, 1960, Perceptron simulation experiments, Proc IRE, 48, 301, 10.1109/JRPROC.1960.287598 Widrow, 1960 Minsky, 1988, Am J Psychol, 84, 449 Tank, 1987, Neural computation by concentrating information in time, Proc Natl Acad Sci USA, 84, 1896, 10.1073/pnas.84.7.1896 Werbos, 1990, Backpropagation through time: what it does and how to do it, Proc IEEE, 78, 1550, 10.1109/5.58337 Sussmann, 1988, Learning algorithms for Boltzmann machines, 27th IEEE conference on decision and control, 1, 786, 10.1109/CDC.1988.194417 Vapnik, 1998, An overview of statistical learning theory, IEEE Trans Neural Netw, 10, 988, 10.1109/72.788640 Smolensky, 1986 Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0 Hihi, 1995, Hierarchical recurrent neural networks for Long-Term dependencies, Adv Neural Inf Process Syst, 8, 493 Hochreiter, 1997, Long short-Term memory, Neural Comput, 9, 1735, 10.1162/neco.1997.9.8.1735 Lécun, 1998, Gradient-based learning applied to document recognition, Proc IEEE, 86, 2278, 10.1109/5.726791 Hinton, 2006, Reducing the dimensionality of data with neural network, Science, 313, 504, 10.1126/science.1127647 Hinton, 2014, A fast learning algorithm for deep belief nets, Neural Comput, 18, 1527, 10.1162/neco.2006.18.7.1527 Deng, 2010, Binary coding of speech spectrograms using a deep auto-encoder, Proceedings of 11th annual conference of the international speech communication association, 3, 1692 Schölkopf, 2006, Efficient learning of sparse representations with an energy-Based model, Proceedings of advances in neural information processingsystems, 1137 Ranzato, 2007, Sparse feature learning for deep belief networks, Proceedings of international conference on neural information processing systems, 20, 1185 Salakhutdinov, 2009, Deep Boltzmann machines, J Mach Learn Res, 5, 1967 Larochelle, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371 Krizhevsky, 2012, ImageNet classification with deep convolution neural networks, International conference on neural information processing systems, 25, 1097 Goodfellow, 2014, Generative adversarial nets, Int Conf Neural Inf Process Syst, 3, 2672 Wang, 2016, Attention-based LSTM for aspect-level sentiment classification, Proceedings of conference on empirical methods in natural language processing, 606, 10.18653/v1/D16-1058 Poggio, 2003, The mathematics of learning: dealing with data, Not Am Math Soc, 50, 537 Kusiak, 2017, Smart manufacturing must embrace big data, Nature, 544, 23, 10.1038/544023a Ince, 2016, Real-time motor fault detection by 1-D convolution neural networks, IEEE Trans Ind Electron, 63, 7067, 10.1109/TIE.2016.2582729 Hassanzadeh, 2017, Unsupervised multi-manifold classification of hyperspectral remote sensing images with contractive Autoencoder, Neurocomputing, 257, 67 Caffe2. https://caffe2.ai/. 2017 [Accessed 20 October 2017]. Theano. http://deeplearning.net/software/theano/index.html#. 2017 [Accessed 20 October 2017]. Google TensorFlow. https://www.tensorflow.org/. 2017 [Accessed 20 October 2017]. Pytorch. http://pytorch.org/. 2017 [Accessed 20 October 2017]. Microsoft Cognitive Toolkit. https://www.microsoft.com/en-us/cognitive-toolkit. 2017 [Accessed 20 October 2017]. Google Google cloud machine learning. https://cloud.google.com/products/machine-learning/. 2017 [Accessed 20 October 2017]. Amazon Web Service. Amazon AI, https://aws.amazon.com/amazon-ai/. 2017 [Accessed 20 October 2017]. 2017 IBM, 2017 Zhang, 2017, Comprehensive overview on computational intelligence techniques for machinery condition monitoring and fault diagnosis, Chin J Mech Eng, 30, 1, 10.3901/JME.2017.19.001 Lee, 2013, Recent advances and trends in predictive manufacturing systems in big data environment, Manuf Lettersm, 1, 38, 10.1016/j.mfglet.2013.09.005 Harding, 2006, Data mining in manufacturing: a review, J Manuf Sci Eng, 128, 969, 10.1115/1.2194554 Esmaeilian, 2016, The evolution and future of manufacturing: a review, J Manuf Syst, 39, 79, 10.1016/j.jmsy.2016.03.001 Kang, 2016, Smart manufacturing: past research, present findings, and future directions, Int J Precision Eng Manuf Green Technol, 3, 111, 10.1007/s40684-016-0015-5 Hazen, 2014, Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications, Int J Prod Econ, 154, 72, 10.1016/j.ijpe.2014.04.018 Shin, 2014, Predictive analytics model for power consumption in manufacturing, Procedia CIRP, 15, 153, 10.1016/j.procir.2014.06.036 Vogl, 2016, A review of diagnostic and prognostic capabilities and best practice for manufacturing, J Intell Manuf, 1 Xie, 2008, A review of recent advances in surface defect detection using texture analysis techniques, Elcvia Electron Lett ComputVision Image Anal, 7, 1, 10.5565/rev/elcvia.268 Neogi, 2014, Review of vision-based steel surface inspection systems, EURASIP J Image Video Process, 1, 1 Pernkopf, 2002, Visual inspection of machined metallic high-precision surfaces, EURASIP J Adv Signal Process, 7, 667 Scholz-Reiter, 2012, Automated surface inspection of cold-formed micro-parts, CIRP Ann Manuf Technol, 61, 531, 10.1016/j.cirp.2012.03.131 Weimer, 2016, Design of deep convolution neural network architectures for automated feature extraction in industrial inspection, CIRP Ann Manuf Technol, 65, 417, 10.1016/j.cirp.2016.04.072 Ren, 2017, A generic deep-learning-based approach for automated surface inspection, IEEE Trans Cybern, 99, 1 Masci, 2012, Steel defect classification with max-pooling convolution neural networks, IEEE international joint conference on neural networks (IJCNN), 20, 1 Park, 2016, Machine learning-based imaging system for surface defect inspection, Int J Precision Eng Manuf Green Technol, 3, 303, 10.1007/s40684-016-0039-x Zhao, 2016 Janssens, 2016, Convolution neural network based fault detection for rotating machinery, J Sound Vib, 377, 331, 10.1016/j.jsv.2016.05.027 Lu, 2017, Intelligent fault diagnosis of rolling bearing using hierarchical convolution network based health state classification, Adv Eng Inf, 32, 139, 10.1016/j.aei.2017.02.005 Guo, 2016, Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis, Measurement, 93, 490, 10.1016/j.measurement.2016.07.054 Verstraete, 2017, Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings, Shock Vib, 1 Chen, 2015, Gearbox fault identification and classification with convolution neural networks, Shock Vib, 2, 1 Wang, 2017, Virtualization and deep recognition for system fault classification, J Manuf Syst, 44, 310, 10.1016/j.jmsy.2017.04.012 Dong, 2016, Small fault diagnosis of front-end speed controlled wind generator based on deep learning, WESEAS Trans Circuits Syst, 15, 64 Wang, 2016, A multi-scale convolution neural network for featureless fault diagnosis, Proceedings of 2016 international symposium on flexible automation, 65, 10.1109/ISFA.2016.7790137 Tamilselvan, 2013, Failure diagnosis using deep belief learning based health state classification, Reliab Eng Syst Saf, 115, 124, 10.1016/j.ress.2013.02.022 Yu, 2015, Nonlinear Gaussian belief network based fault diagnosis for industrial processes, J Process Control, 35, 178, 10.1016/j.jprocont.2015.09.004 Tran, 2014, An approach to fault diagnosis of reciprocating compressor valves using teager–kaiser energy operator and deep belief networks, Expert Syst Appl, 41, 4113, 10.1016/j.eswa.2013.12.026 Shao, 2015, Rolling bearing fault diagnosis using an optimization deep belief network, Meas Sci Technol, 26, 1, 10.1088/0957-0233/26/11/115002 Gan, 2016, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, Mech Syst Signal Process, 72–73, 92, 10.1016/j.ymssp.2015.11.014 Yin, 2016, Fault diagnosis network design for vehicle on-board equipments of high speed railway: a deep learning approach, Eng Appl Artif Intell, 56, 250, 10.1016/j.engappai.2016.10.002 Xie, 2015, Fault diagnosis in high-speed train running gears with improved deep belief networks, J Comput Inf Syst, 11, 7723 Li, 2016, Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals, Mech Syst Signal Process, 7, 6 Jia, 2016, Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech Syst Signal Process, 7, 2 Guo, 2014, Structural health monitoring by using a sparse coding −based deep learning algorithm with wireless sensor networks, Pers Ubiquit Comput, 18, 1977, 10.1007/s00779-014-0800-5 Lu, 2017, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification, Signal Process, 130, 377, 10.1016/j.sigpro.2016.07.028 Shao, 2017, An enhancement deep feature fusion method for rotating machinery fault diagnosis, Knowl Based Syst, 119, 200, 10.1016/j.knosys.2016.12.012 Sun, 2016, A sparse auto-encoder-based deep neural network approach for induction motor faults classification, Measurement, 89, 171, 10.1016/j.measurement.2016.04.007 Yang, 2016, Representational learning for fault diagnosis of wind turbine equipment: a multi-layered extreme learning machines approach, Energies, 9, 1 Wang, 2016, Transformer fault diagnosis using continuous sparse autoencoder, SpingerPlus, 5, 1 Lei, 2016, An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data, IEEE Trans Ind Electron, 63, 3137, 10.1109/TIE.2016.2519325 Li, 2015, Multimodel deep support vector classification with homologous features and its application to gearbox fault diagnosis, Neurocomputing, 168, 119, 10.1016/j.neucom.2015.06.008 Guo, 2017, Deep fault recognizer: an integrated model to denoise and extract features for fault diagnosis in rotating machinery, Appl Sci, 7, 1 Chen, 2017, Deep neural network-based rolling bearing fault diagnosis, Microelectron Reliab, 75, 327, 10.1016/j.microrel.2017.03.006 Malhi, 2011, Prognosis of defect propagation based on recurrent neural networks, IEEE Trans Instrum Meas, 60, 703, 10.1109/TIM.2010.2078296 Zhao, 2018, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Transa Ind Electron, 65, 1539, 10.1109/TIE.2017.2733438 Zhao, 2017, Learning to monitor machine health with convolution bi-directional LSTM networks, Sensors, 17, 1 Wu, 2017, Remaining useful life estimation of engineered systems using vanilla LSTM neural networks, Neurocomputing, 226, 853 Malhotra, 2015, Long short term memory networks for anomaly detection in time series, 89 Wang, 2017, A deep learning-based approach to material removal rate prediction in polishing, CIRP Ann Manuf Technol, 66, 429, 10.1016/j.cirp.2017.04.013 Deutsch, 2017, Remaining useful life prediction of hybrid ceramic bearings using an integrated deep learning and particle filter approach, Appl Sci, 7, 1 Qiu, 2014, Ensemble deep learning for regression and time series forecasting, IEEE symposium series on computational intelligence, 1 Zhang, 2017, Resource requests prediction in the cloud computing environment with a deep belief network, Software Pract Exp, 47, 473, 10.1002/spe.2426 Khan, 2017, Cost-sensitive learning of deep feature representations from imbalanced data, IEEE Trans Neural Networks Learn Syst, 99, 1 Maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579 Yu, 2013, KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition, IEEE international conference on acoustics, speech and signal processing, 7893, 10.1109/ICASSP.2013.6639201 Vig, 2014, Large-scale optimization of hierarchical features for saliency prediction in natural images, IEEE computer vision and pattern recognition, 2798 Pan, 2010, A survey on transfer learning, IEEE Trans Knowl Data Eng, 22, 1345, 10.1109/TKDE.2009.191 Dziugaite, 2015, Training generative neural networks via Maximum Mean Discrepancy optimization, Proceedings of the 31st conference on uncertainty in artificial intelligence, 258 Mell, 2009, The NIST definition of cloud computing, Commun ACM, 53 Davis, 2012, Smart manufacturing, manufacturing intelligence and demand-dynamic performance, Comput Chem Eng, 47, 145, 10.1016/j.compchemeng.2012.06.037 Lee, 2014, Service innovation and smart analytics for industry 4: 0 and big data environment, Procedia CIRP, 16, 3, 10.1016/j.procir.2014.02.001 Lee, 2013, Recent advances and trends in predictive manufacturing systems in big data environment, Manuf Lett, 1, 38, 10.1016/j.mfglet.2013.09.005 Chen, 2014, Data-intensive applications, challenges, techniques and technologies: a survey on Big Data, Inf Sci, 275, 314, 10.1016/j.ins.2014.01.015 Meziane, 2000, Intelligent systems in manufacturing: current developments and future prospects, Integr Manuf Syst, 11, 218, 10.1108/09576060010326221 O’Donovan, 2015, Big data in manufacturing: a systematic mapping study, J Big Data, 2, 1, 10.1186/s40537-015-0028-x Wu, 2017, A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing, J Manuf Syst, 43, 25, 10.1016/j.jmsy.2017.02.011