A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools
Abstract
:1. Brief Introduction
2. Various Learning Algorithms
2.1. Recurrent Neural Networks (RNNs)
2.2. Convolutional Neural Networks (CNNs)
2.3. Deep Neural Networks (DNNs)
2.4. The Other Methods
3. Diagnosis and Detection of Mechanical Components
3.1. Bearings
3.2. Gearboxes
3.3. Aircraft
3.4. Rotors
3.5. Other Mechanical Components
4. AI Applications in Smart Machine Tools
4.1. Intelligent Manufacturing
4.2. Prognosis of Mechanical Components
4.3. Smart Sensors
5. Conclusions, Current Challenges, and Future Work
Acknowledgments
Conflicts of Interest
References
- Yin, S.; Li, X.; Gao, H.; Kaynak, O. Data-based techniques focused on modern industry: An overview. IEEE Trans. Ind. Electron. 2015, 62, 657–667. [Google Scholar] [CrossRef]
- Jeschke, S.B.C.; Song, H.; Rawat, D.B. Industrial Internet of Things: Cybermanufacturing Systems; Springer: Berlin, Germany, 2016. [Google Scholar]
- Lund, D.; MacGillivray, C.; Turner, V.; Morales, M. Worldwide and Regional Internet of Things (Iot) 2014–2020 Forecast: A Virtuous Circle of Proven Value and Demand; Technical Report; International Data Corporation (IDC): Framingham, MA, USA, 2014. [Google Scholar]
- Giles, C.L.; Miller, C.B.; Chen, D.; Chen, H.H.; Sun, G.Z.; Lee, Y.C. Learning and extracting finite state automata with second-order. Neural Comput. 1992, 4, 13. [Google Scholar] [CrossRef]
- Funahashi, K.-I.; Nakamura, Y. Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 1993, 6, 801–806. [Google Scholar] [CrossRef]
- Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural Comput. 1997, 9, 1735–1780. [Google Scholar] [CrossRef] [PubMed]
- Gers, F.A.; Schmidhuber, J.; Cummins, F. Learning to forget: Continual prediction with LSTM. Neural Comput. 2000, 12, 2451–2471. [Google Scholar] [CrossRef] [PubMed]
- Jaeger, H. Tutorial on Training Recurrent Neural Networks, Covering BPPT, RTRL, EKF and the Echo State Network Approach; GMD Report; German, National Research Center for Information Technology: St. Augustin, Germany, 2002. [Google Scholar]
- Gers, F.A.; Schraudolph, N.N.; Schmidhuber, J. Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 2003, 3, 115–143. [Google Scholar]
- Cho, K.; van Merrienboer, B.; Gulcehre, C.l.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning phrase representations using RNN encoder—Decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014; pp. 1724–1734. [Google Scholar]
- Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv, 2014; arXiv:1412.3555. [Google Scholar]
- Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85–117. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, R.; Wang, J.; Yan, R.; Mao, K. Machine health monitoring with LSTM networks. In Proceedings of the 10th International Conference on Sensing Technology (ICST), Nanjing, China, 11–13 November 2016; pp. 1–6. [Google Scholar] [CrossRef]
- Zhao, R.; Yan, R.; Wang, J.; Mao, K. Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 2017, 17, 273. [Google Scholar] [CrossRef] [PubMed]
- Cun, Y.L.; Boser, B.; Denker, J.S.; Howard, R.E.; Habbard, W.; Jackel, L.D.; Henderson, D. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems 2; David, S.T., Ed.; Morgan Kaufmann Publishers Inc.: Burlington, MA, USA, 1990; pp. 396–404. [Google Scholar]
- LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278–2324. [Google Scholar] [CrossRef] [Green Version]
- Jarrett, K.; Kavukcuoglu, K.; Ranzato, M.A.; Lecun, Y. What is the best multi-stage architecture for object recognition? In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision 2009, Kyoto, Japan, 29 September–2 October 2009; Volume 12. [Google Scholar]
- Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; Li, F.-F. ImageNet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009; pp. 248–255. [Google Scholar] [CrossRef]
- Sermanet, P.; Chintala, S.; Lecun, Y. Convolutional neural networks applied to house numbers digit classification. In Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 11–15 November 2012; p. 4. [Google Scholar]
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, 3–6 December 2012; Volume 1, pp. 1097–1105. [Google Scholar]
- Abdel-Hamid, O.; Mohamed, A.R.; Jiang, H.; Penn, G. Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. In Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 25–30 March 2012; pp. 4277–4280. [Google Scholar]
- Kim, Y. Convolutional neural networks for sentence classification. arXiv, 2014; arXiv:1408.5882. [Google Scholar]
- He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 770–778. [Google Scholar]
- Yan, Y.; Chen, M.; Shyu, M.-L.; Chen, S.-C. Deep learning for imbalanced multimedia data classification. In Proceedings of the 2015 IEEE International Symposium on Multimedia (ISM), Miami, FL, USA, 14–16 December 2015; pp. 483–488. [Google Scholar]
- Ren, S.; He, K.; Girshick, R.; Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. arXiv, 2015; arXiv:1506.01497. [Google Scholar]
- Babu, G.S.; Zhao, P.; Li, X.-L. Deep convolutional neural network based regression approach for estimation of remaining useful life. In Proceedings of the DASFAA 2016, Dallas, TX, USA, 16–19 April 2016; Navathe, S.B., Wu, W., Shekhar, S., Du, X., Wang, X.S., Xiong, H., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 214–228. [Google Scholar]
- Weimer, D.; Scholz-Reiter, B.; Shpitalni, M. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. 2016, 65, 417–420. [Google Scholar] [CrossRef]
- Huang, C.; Li, Y.; Loy, C.C.; Tang, X. Learning deep representation for imbalanced classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, Las Vegas, NV, USA, 26 June–1 July 2016; pp. 5375–5384. [Google Scholar]
- Collobert, R.; Weston, J. A unified architecture for natural language processing. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 5–9 July 2008; pp. 160–167. [Google Scholar]
- Maaten, L.V.d.; Hinton, G.E. Visualizing Data using t-SNE. J. Mach. Learn. Res. 2008, 9, 2579–2605. [Google Scholar]
- Hinton, G.; Deng, L.; Yu, D.; Dahl, G.; Mohamed, A.-R.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T.; et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag. 2012, 29, 82–97. [Google Scholar] [CrossRef]
- Yosinski, J.; Clune, J.; Nguyen, A.; Fuchs, T.; Lipson, H. Understanding neural networks through deep visualization. In Proceedings of the 31st International Conference on Machine Learning, Lille, France, 21–26 June 2015. [Google Scholar]
- Lu, W.; Liang, B.; Cheng, Y.; Meng, D.; Yang, J.; Zhang, T. Deep model based domain adaptation for fault diagnosis. IEEE Trans. Ind. Electron. 2017, 64, 2296–2305. [Google Scholar] [CrossRef]
- Zhang, C.; Sun, J.H.; Tan, K.C. Deep belief networks ensemble with multi-objective optimization for failure diagnosis. In Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Kowloon, China, 9–12 October 2015; pp. 32–37. [Google Scholar] [CrossRef]
- Liao, L.; Jin, W.; Pavel, R. Enhanced restricted boltzmann machine with prognosability regularization for prognostics and health assessment. IEEE Trans. Ind. Electron. 2016, 63, 7076–7083. [Google Scholar] [CrossRef]
- Zhang, C.; Lim, P.; Qin, A.K.; Tan, K.C. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans. Neural Netw. Learn. Syst. 2017, 28, 2306–2318. [Google Scholar] [CrossRef] [PubMed]
- Hinton, G.; Osindero, S.; Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006, 18, 1527–1554. [Google Scholar] [CrossRef] [PubMed]
- Bengio, Y.; Lamblin, P.; Popovici, D.; Larochelle, H. Greedy layer-wise training of deep networks. In Proceedings of the 19th International Conference on Neural Information Processing Systems, Doha, Qatar, 12–15 November 2012; pp. 153–160. [Google Scholar]
- Salakhutdinov, R.; Hinton, G. Deep boltzmann machines. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, Clearwater Beach, FL, USA, 16–18 April 2009; p. 8. [Google Scholar]
- Ng, A. Sparse Auto-Encoder; CS294A Lecture Notes; Stanford University: Stanford, CA, USA, 2011; p. 19. [Google Scholar]
- Bengio, Y.; Courville, A.; Vincent, P. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 2013, 35, 1798–1828. [Google Scholar] [CrossRef] [PubMed]
- Cambria, E.; Huang, G.-B.; Kasun, L.L.C.; Zhou, H.; Vong, C.M.; Lin, J.; Yin, J.; Cai, Z.; Liu, Q.; Li, K.; et al. Extreme learning machines [trends & controversies]. IEEE Intell. Syst. 2013, 28, 30–59. [Google Scholar]
- Thirukovalluru, R.; Dixit, S.; Sevakula, R.; Verma, N.K.; Salour, A. Generating feature sets for fault diagnosis using denoising stacked auto-encoder. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada, 20–22 June 2016; pp. 1–7. [Google Scholar]
- Kishore, R.; Reddy, K.; Sarkar, S.; Giering, M. Anomaly detection and fault disambiguation in large flight data: A multi-modal deep auto-encoder approach. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, Denver, CO, USA, 3–6 October 2016. [Google Scholar]
- Wang, L.; Zhao, X.; Pei, J.; Tang, G. Transformer fault diagnosis using continuous sparse autoencoder. Springerplus 2016, 5, 448. [Google Scholar] [CrossRef] [PubMed]
- Pan, S.J.; Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 2010, 22, 1345–1359. [Google Scholar] [CrossRef]
- Leung, M.K.; Xiong, H.Y.; Lee, L.J.; Frey, B.J. Deep learning of the tissue-regulated splicing code. Bioinformatics 2014, 30, i121–i129. [Google Scholar] [CrossRef] [PubMed]
- Deng, L.; Dong, Y. Deep learning: Methods and applications. Found. Trends Signal Process. 2014, 7, 197–387. [Google Scholar] [CrossRef]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Zhao, R.; Yan, R.; Chen, Z.; Mao, K.; Wang, P.; Gao, R. Deep learning and its applications to machine health monitoring: A survey. arXiv, 2015; arXiv:1612.07640. [Google Scholar]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep Learning; MIT Press: Cambridge, MA, USA, 2016. [Google Scholar]
- Hinton, G.E. Learning multiple layers of representation. Trends Cogn. Sci. 2007, 11, 428–434. [Google Scholar] [CrossRef] [PubMed]
- Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P.-A. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 5–9 July 2008; pp. 1096–1103. [Google Scholar]
- Raina, R.; Madhavan, A.; Ng, A.Y. Large-scale deep unsupervised learning using graphics processors. In Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada, 14–18 June 2009; pp. 873–880. [Google Scholar]
- Le, Q.V. Building high-level features using large scale unsupervised learning. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Edinburgh, UK, 26–31 May 2013; pp. 8595–8598. [Google Scholar]
- Srivastava, N.; Mansimov, E.; Salakhutdinov, R. Unsupervised learning of video representations using LSTMs. In Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, 6–11 July 2015; Volume 37, pp. 843–852. [Google Scholar]
- Abe, H.; Yamaguchi, T. Constructive meta-learning with machine learning method repositories. In Proceedings of the 17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Ottawa, ON, Canada, 17–20 May 2004; Orchard, B., Yang, C., Ali, M., Eds.; Springer: Berlin/Heidelberg, Germany, 2004; pp. 502–511. [Google Scholar]
- Ali, S.; Smith-Miles, K.A. A meta-learning approach to automatic kernel selection for support vector machines. Neurocomputing 2006, 70, 173–186. [Google Scholar] [CrossRef]
- Gomes, T.A.F.; Prudêncio, R.B.C.; Soares, C.; Rossi, A.L.D.; Carvalho, A. Combining meta-learning and search techniques to select parameters for support vector machines. Neurocomputing 2012, 75, 3–13. [Google Scholar] [CrossRef] [Green Version]
- Feurer, M.; Springenberg, J.T.; Hutter, F. Using meta-learning to initialize bayesian optimization of hyperparameters. In Proceedings of the 2014 International Conference on META-Learning and Algorithm Selection, Prague, Czech Republic, 19 September 2014; Volume 1201, pp. 3–10. [Google Scholar]
- Ferrari, D.G.; de Castro, L.N. Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods. Inf. Sci. 2015, 301, 181–194. [Google Scholar] [CrossRef]
- García, S.; Luengo, J.; Sáez, J.A.; López, V.; Herrera, F. A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning. IEEE Trans. Knowl. Data Eng. 2013, 25, 734–750. [Google Scholar] [CrossRef]
- Han, J.; Zhang, D.; Cheng, G.; Guo, L.; Ren, J. Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote Sens. 2015, 53, 3325–3337. [Google Scholar] [CrossRef]
- Munos, R.; Stepleton, T.; Harutyunyan, A.; Bellemare, M.G. Safe and efficient off-policy reinforcement learning. arXiv, 2016; arXiv:1606.02647. [Google Scholar]
- Lample, G.; Chaplot, D.S. Playing FPS games with deep reinforcement learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA USA, 4–5 February 2017. [Google Scholar]
- Arulkumaran, K.; Deisenroth, M.P.; Brundage, M.; Bharath, A.A. A brief survey of deep reinforcement learning. arXiv, 2017; arXiv:1708.05866. [Google Scholar]
- Watkins, C.J.C.H.; Dayan, P. Technical note: Q-learning. Mach. Learn. 1992, 8, 279–292. [Google Scholar] [CrossRef]
- Li, B.; Chow, M.Y.; Tipsuwan, Y.; Hung, J.C. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron. 2000, 47, 1060–1069. [Google Scholar] [CrossRef]
- Samanta, B.; Al-Balushi, K.R. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process. 2003, 17, 317–328. [Google Scholar] [CrossRef]
- Malhi, A.; Gao, R.X. PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas. 2004, 53, 1517–1525. [Google Scholar] [CrossRef]
- Widodo, A.; Yang, B.-S. Support vector machine in machine condition monitoring and fault diagnosis. Mech. Syst. Signal Process. 2007, 21, 2560–2574. [Google Scholar] [CrossRef]
- Su, H.; Chong, K.T. Induction machine condition monitoring using neural network modeling. IEEE Trans. Ind. Electron. 2007, 54, 241–249. [Google Scholar] [CrossRef]
- Seryasat, O.R.; Haddadnia, J.; Arabnia, Y.; Zeinali, M.; Abooalizadeh, Z.; Taherkhani, A.; Tabrizy, S.; Maleki, F. Intelligent fault detection of ball-bearings using artificial neural networks and support-vector machine. Life Sci. J. 2012, 9, 4186–4189. [Google Scholar]
- Tao, S.; Zhang, T.; Yang, J.; Wang, X.; Lu, W. Bearing fault diagnosis method based on stacked autoencoder and softmax regression. In Proceedings of the 2015 34th Chinese Control Conference (CCC), Hangzhou, China, 28–30 July 2015; pp. 6331–6335. [Google Scholar]
- Junbo, T.; Weining, L.; Juneng, A.; Xueqian, W. Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder. In Proceedings of the 27th Chinese Control and Decision Conference (2015 CCDC), Qingdao, China, 23–25 May 2015; pp. 4608–4613. [Google Scholar]
- Lu, W.; Wang, X.; Yang, C.; Zhang, T. A novel feature extraction method using deep neural network for rolling bearing fault diagnosis. In Proceedings of the 27th Chinese Control and Decision Conference (2015 CCDC), Qingdao, China, 23–25 May 2015; pp. 2427–2431. [Google Scholar]
- Fei, S.; Chao, C.; Yan, R.; Gao, R.X. Bearing fault diagnosis based on SVD feature extraction and transfer learning classification. In Proceedings of the 2015 Prognostics and System Health Management Conference (PHM), Beijing, China, 21–23 October 2015; pp. 1–6. [Google Scholar]
- Jia, F.; Lei, Y.; Lin, J.; Zhou, X.; Lu, N. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 2016, 72, 303–315. [Google Scholar] [CrossRef]
- Liu, H.; Li, L.; Ma, J. Rolling bearing fault diagnosis based on STFT-deep learning and sound signals. Shock Vib. 2016, 2016. [Google Scholar] [CrossRef]
- Guo, L.; Gao, H.; Huang, H.; He, X.; Li, S. Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring. Shock Vib. 2016, 2016. [Google Scholar] [CrossRef]
- Mao, W.; He, J.; Li, Y.; Yan, Y. Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2017, 231, 1560–1578. [Google Scholar] [CrossRef]
- Ma, M.; Chen, X.; Wang, S.; Liu, Y.; Li, W. Bearing degradation assessment based on weibull distribution and deep belief network. In Proceedings of the 2016 International Symposium on Flexible Automation (ISFA), Cleveland, OH, USA, 1–3 August 2016; pp. 382–385. [Google Scholar]
- Tao, J.; Liu, Y.; Yang, D. Bearing fault diagnosis based on deep belief network and multisensor information fusion. Shock Vib. 2016, 2016. [Google Scholar] [CrossRef]
- Gan, M.; Wang, C.; Zhu, C.A. 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. 2016, 72, 92–104. [Google Scholar] [CrossRef]
- Oh, H.; Jeon, B.C.; Jung, J.H.; Youn, B.D. Smart diagnosis of journal bearing rotor systems unsupervised feature extraction scheme by deep learning. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, Denver, CO, UAS, 3–6 October 2016; p. 8. [Google Scholar]
- Janssens, O.; Slavkovikj, V.; Vervisch, B.; Stockman, K.; Loccufier, M.; Verstockt, S.; van de Walle, R.; van Hoecke, S. Convolutional neural network based fault detection for rotating machinery. J. Sound Vib. 2016, 377, 331–345. [Google Scholar] [CrossRef]
- Guo, X.; Chen, L.; Shen, C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 2016, 93, 490–502. [Google Scholar] [CrossRef]
- Lu, C.; Wang, Z.-Y.; Qin, W.-L.; Ma, J. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process. 2017, 130, 377–388. [Google Scholar] [CrossRef]
- Chen, Z.; Li, W. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans. Instrum. Meas. 2017, 66, 1693–1702. [Google Scholar] [CrossRef]
- Ding, X.; He, Q. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans. Instrum. Meas. 2017, 66, 1926–1935. [Google Scholar] [CrossRef]
- Mao, W.; He, L.; Yan, Y.; Wang, J. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine. Mech. Syst. Signal Process. 2017, 83, 450–473. [Google Scholar] [CrossRef]
- Rafiee, J.; Arvani, F.; Harifi, A.; Sadeghi, M.H. Intelligent condition monitoring of a gearbox using artificial neural network. Mech. Syst. Signal Process. 2007, 21, 1746–1754. [Google Scholar] [CrossRef]
- Zhang, Z.; Wang, Y.; Wang, K. Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. Int. J. Adv. Manuf. Technol. 2013, 68, 763–773. [Google Scholar] [CrossRef]
- Li, C.; Sanchez, R.-V.; Zurita, G.; Cerrada, M.; Cabrera, D.; Vásquez, R.E. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 2015, 168, 119–127. [Google Scholar] [CrossRef]
- Chen, Z.; Li, C.; Sánchez, R.V. Multi-Layer neural network with deep belief network for gearbox fault diagnosis. J. Vibroeng. 2015, 17, 2379–2392. [Google Scholar]
- Chen, Z.; Li, C.; Sanchez, R.-V. Gearbox fault identification and classification with convolutional neural networks. Shock. Vib. 2015, 2015, 10. [Google Scholar] [CrossRef]
- Li, C.; Sánchez, R.-V.; Zurita, G.; Cerrada, M.; Cabrera, D. Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors 2016, 16, 895. [Google Scholar] [CrossRef] [PubMed]
- Li, C.; Sanchez, R.-V.; Zurita, G.; Cerrada, M.; Cabrera, D.; Vásquez, R.E. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mech. Syst. Signal Process. 2016, 76, 283–293. [Google Scholar] [CrossRef]
- Xie, J.; Zhang, L.; Duan, L.; Wang, J. On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on transfer component analysis. In Proceedings of the 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), Ottawa, ON, Canada, 20–22 June 2016; pp. 1–6. [Google Scholar]
- Tamilselvan, P.; Wang, Y.; Wang, P. Deep Belief Network based state classification for structural health diagnosis. In Proceedings of the 2012 IEEE Aerospace Conference, Big Sky, MT, USA, 3–10 March 2012; pp. 1–11. [Google Scholar]
- Tamilselvan, P.; Wang, P. Failure diagnosis using deep belief learning based health state classification. Reliab. Eng. Syst. Saf. 2013, 115, 124–135. [Google Scholar] [CrossRef]
- Li, K.; Wang, Q. Study on signal recognition and diagnosis for spacecraft based on deep learning method. In Proceedings of the 2015 Prognostics and System Health Management Conference (PHM), Beijing, China, 21–23 October 2015; pp. 1–5. [Google Scholar]
- Yuan, M.; Wu, Y.; Lin, L. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network. In Proceedings of the 2016 IEEE International Conference on Aircraft Utility Systems (AUS), Beijing, China, 10–12 October 2016; pp. 135–140. [Google Scholar]
- Oppenheimer, C.H.; Loparo, K.A. Physically based diagnosis and prognosis of cracked rotor shafts. Proc. SPIE 2002, 4733, 122–132. [Google Scholar] [CrossRef]
- Wang, J.; Zhuang, J.; Duan, L.; Cheng, W. A multi-scale convolution neural network for featureless fault diagnosis. In Proceedings of the 2016 International Symposium on Flexible Automation (ISFA), Cleveland, OH, USA, 1–3 August 2016; pp. 65–70. [Google Scholar]
- Duan, L.; Xie, M.; Bai, T.; Wang, J. A new support vector data description method for machinery fault diagnosis with unbalanced datasets. Expert Syst. Appl. 2016, 64, 239–246. [Google Scholar] [CrossRef]
- Sun, W.; Shao, S.; Zhao, R.; Yan, R.; Zhang, X.; Chen, X. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 2016, 89, 171–178. [Google Scholar] [CrossRef]
- Shao, S.; Sun, W.; Wang, P.; Gao, R.X.; Yan, R. Learning features from vibration signals for induction motor fault diagnosis. In Proceedings of the 2016 International Symposium on Flexible Automation (ISFA), Cleveland, OH, USA, 1–3 August 2016; pp. 71–76. [Google Scholar]
- Ince, T.; Kiranyaz, S.; Eren, L.; Askar, M.; Gabbouj, M. Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans. Ind. Electron. 2016, 63, 7067–7075. [Google Scholar] [CrossRef]
- Kuo, R.J. Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic. Eng. Appl. Artif. Intell. 1995, 8, 25–34. [Google Scholar] [CrossRef]
- Aminian, M.; Aminian, F. Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Trans. Circuits Syst. II Analog Digit. Signal Process. 2000, 47, 151–156. [Google Scholar] [CrossRef]
- Yan, J.; Lee, J. Degradation assessment and fault modes classification using logistic regression. J. Manuf. Sci. Eng. 2004, 127, 912–914. [Google Scholar] [CrossRef]
- Muralidharan, V.; Sugumaran, V. A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Appl. Soft Comput. 2012, 12, 2023–2029. [Google Scholar] [CrossRef]
- Verma, N.K.; Gupta, V.K.; Sharma, M.; Sevakula, R.K. Intelligent condition based monitoring of rotating machines using sparse auto-encoders. In Proceedings of the 2013 IEEE Conference on Prognostics and Health Management (PHM), Gaithersburg, MD, USA, 24–27 June 2013; pp. 1–7. [Google Scholar]
- Zhu, H.; Rui, T.; Wang, X.; Zhou, Y.; Fang, H. Fault diagnosis of hydraulic pump based on stacked autoencoders. In Proceedings of the 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Qingdao, China, 16–18 July 2015; pp. 58–62. [Google Scholar]
- Wang, J.; Xie, J.; Zhao, R.; Mao, K.; Zhang, L. A new probabilistic kernel factor analysis for multisensory data fusion: Application to tool condition monitoring. IEEE Trans. Instrum. Meas. 2016, 65, 2527–2537. [Google Scholar] [CrossRef]
- Galloway, G.S.; Catterson, V.M.; Fay, T.; Robb, A.; Love, C. Diagnosis of tidal turbine vibration data through deep neural networks. In Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016; Eballard, I., Bregon, A., Eds.; PHM Society: New York, USA, 2016; pp. 172–180. [Google Scholar]
- Dong, H.Y.; Yang, L.X.; Li, H.W. Small fault diagnosis of front-end speed controlled wind generator based on deep learning. WSEAS Trans. Circuits Syst. 2016, 15, 9. [Google Scholar]
- Abdeljaber, O.; Avci, O.; Kiranyaz, S.; Gabbouj, M.; Inman, D.J. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound Vib. 2017, 388, 154–170. [Google Scholar] [CrossRef]
- Monostori, L.; Barschdorff, D. Artificial neural networks in intelligent manufacturing. Robot. Comput. Integr. Manuf. 1992, 9, 17. [Google Scholar]
- Chang, P.-T.; Chang, C.-H. An integrated artificial intelligent computer-aided process planning system. Int. J. Comput. Integr. Manuf. 2000, 13, 483–497. [Google Scholar] [CrossRef]
- Sta, O.; Tolnay, M.N.; Magdolen, L. Application of artificial intelligence in manufacturing systems. In Mechanical and Electronics Engineering; World Scientific: Singapore, 2009; pp. 27–30. [Google Scholar]
- Hwang, R.-C.; Chen, Y.-J.; Huang, H.-C. Artificial intelligent analyzer for mechanical properties of rolled steel bar by using neural networks. Expert Syst. Appl. 2010, 37, 3136–3139. [Google Scholar] [CrossRef]
- Lambiase, F. Optimization of shape rolling sequences by integrated artificial intelligent techniques. Int. J. Adv. Manuf. Technol. 2013, 68, 443–452. [Google Scholar] [CrossRef]
- Fu, Y.; Zhang, Y.; Qiao, H.; Li, D.; Zhou, H.; Leopold, J. Analysis of feature extracting ability for cutting state monitoring using deep belief networks. Procedia CIRP 2015, 31, 29–34. [Google Scholar] [CrossRef]
- Wang, J.; Xie, J.; Zhao, R.; Zhang, L.; Duan, L. Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robot. Comput.-Integr. Manuf. 2017, 45, 47–58. [Google Scholar] [CrossRef]
- Li, H.-X.; Si, H. Control for intelligent manufacturing: a multiscale challenge. Engineering 2017, 3, 608–615. [Google Scholar] [CrossRef]
- Li, B.-H.; Hou, B.-C.; Yu, W.-T.; Lu, X.-B.; Yang, C.-W. Applications of artificial intelligence in intelligent manufacturing: A review. Front. Inf. Technol. Electron. Eng. 2017, 18, 86–96. [Google Scholar] [CrossRef]
- Kuo, R.J.; Cohen, P.H. Intelligent tool wear estimation system through artificial neural networks and fuzzy modeling. Artif. Intell. Eng. 1998, 12, 229–242. [Google Scholar] [CrossRef]
- Li, X.Q.; Wong, Y.S.; Nee, A.Y.C. Intelligent tool wear identification based on optical scattering image and hybrid artificial intelligence techniques. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 1999, 213, 191–196. [Google Scholar] [CrossRef]
- Kim, S.-D.; Shin, D.-H.; Lim, L.-M.; Lee, J.; Kim, S.-H. Designed strength identification of concrete by ultrasonic signal processing based on artificial intelligence techniques. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 2005, 52, 1145–1151. [Google Scholar] [PubMed]
- Norouzi, A.; Hamedi, M.; Adineh, V.R. Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods. Int. J. Adv. Manuf. Technol. 2012, 61, 135–147. [Google Scholar] [CrossRef]
- Irgolic, T.; Cus, F.; Paulic, M.; Balic, J. Prediction of cutting forces with neural network by milling functionally graded material. Procedia Eng. 2014, 69, 804–813. [Google Scholar] [CrossRef]
- Hanief, M.; Wani, M.F.; Charoo, M.S. Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis. Eng. Sci. Technol. Int. J. 2017, 20, 1220–1226. [Google Scholar] [CrossRef]
- Koker, R.; Ferikoglu, A. Model based intelligent control of a 3-joint robotic manipulator: A simulation study using artificial neural networks. In Proceedings of the 19th International Symposium, Kemer-Antalya, Turkey, 27–29 October 2004; Aykanat, C., Dayar, T., Körpeoğlu, İ., Eds.; Springer: Berlin/Heidelberg, Germany, 2004; pp. 31–40. [Google Scholar]
- Hamedi, M. Intelligent fixture design through a hybrid system of artificial neural network and genetic algorithm. Artif. Intell. Rev. 2005, 23, 295–311. [Google Scholar] [CrossRef]
- Lee, J.; Bagheri, B.; Kao, H.-A. A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manuf. Lett. 2015, 3, 18–23. [Google Scholar] [CrossRef]
- Trappey, A.J.C.; Trappey, C.V.; Govindarajan, U.H.; Sun, J.J.; Chuang, A.C. A review of technology standards and patent portfolios for enabling cyber-physical systems in advanced manufacturing. IEEE Access 2016, 4, 7356–7382. [Google Scholar] [CrossRef]
- Claudi, A.; Sernani, P.; Dolcini, G.; Palazzo, L.; Dragoni, A.F. A hierarchical hybrid model for intelligent cyber-physical systems. In Proceedings of the 11th Workshop on Intelligent Solutions in Embedded Systems (WISES), Regensburg, Germany, 10–11 September 2013; pp. 1–6. [Google Scholar]
- Castaño, F.; Beruvides, G.; Haber, E.R.; Artuñedo, A. Obstacle recognition based on machine learning for On-Chip LiDAR sensors in a Cyber-Physical System. Sensors 2017, 17, 2109. [Google Scholar] [CrossRef] [PubMed]
- Varshney, K.R.; Alemzadeh, H. On the safety of machine learning: cyber-physical systems, decision sciences, and data products. arXiv, 2016; arXiv:1610.01256. [Google Scholar]
- Mahamad, A.K. Diagnosis, Classification and Prognosis of Rotating Machine Using Artificial Intelligence. PhD Thesis, Kumamoto University, Kumamoto, Japan, 2010. [Google Scholar]
- Mahamad, A.K.; Saon, S.; Hiyama, T. Predicting remaining useful life of rotating machinery based artificial neural network. Comput. Math. Appl. 2010, 60, 1078–1087. [Google Scholar] [CrossRef]
- Zhang, Z.; Wang, Y.; Wang, K. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. J. Intell. Manuf. 2013, 24, 1213–1227. [Google Scholar] [CrossRef]
- Li, Y.; Kurfess, T.R.; Liang, S.Y. Stochastic prognostics for rolling element bearings. Mech. Syst. Signal Process. 2000, 14, 747–762. [Google Scholar] [CrossRef]
- Shao, Y.; Nezu, K. Prognosis of remaining bearing life using neural networks. Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 2000, 214, 217–230. [Google Scholar] [CrossRef]
- Deutsch, J.; He, D. Using deep learning-based approaches to predict remaining useful life of rotating components. IEEE Trans. Syst. Man Cybern. Syst. 2018, 48, 11–20. [Google Scholar] [CrossRef]
- Jardine, A.K.S.; Lin, D.; Banjevic, D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech. Syst. Signal Process. 2006, 20, 1483–1510. [Google Scholar] [CrossRef]
- Yu, M.; Wang, D.; Luo, M. Model-based prognosis for hybrid systems with mode-dependent degradation behaviors. IEEE Trans. Ind. Electron. 2014, 61, 546–554. [Google Scholar] [CrossRef]
- Malhotra, P.; Tv, V.; Ramakrishnan, A.; Anand, G.; Vig, L.; Agarwal, P.; Shroff, G. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. arXiv, 2016; arXiv:1608.06154. [Google Scholar]
- Patra, J.C.; Kot, A.C.; Panda, G. An intelligent pressure sensor using neural networks. IEEE Trans. Instrum. Meas. 2000, 49, 829–834. [Google Scholar] [CrossRef]
- Patra, J.C.; van den Bos, A. Modeling of an intelligent pressure sensor using functional link artificial neural networks. ISA Trans. 2000, 39, 15–27. [Google Scholar] [CrossRef]
- Ji, T.; Pang, Q.; Liu, X. An intelligent pressure sensor using rough set neural networks. In Proceedings of the 2006 IEEE International Conference on Information Acquisition, Weihai, China, 20–23 August 2006; pp. 717–721. [Google Scholar]
- Pramanik, C.; Islam, T.; Saha, H. Temperature compensation of piezoresistive micro-machined porous silicon pressure sensor by ANN. Microelectron. Reliab. 2006, 46, 343–351. [Google Scholar] [CrossRef]
- Patra, J.C.; Ang, E.L.; Meher, P.K. A novel neural network-based linearization and auto-compensation technique for sensors. In Proceedings of the 2006 IEEE International Symposium on Circuits and Systems, Kos, Greece, 21–24 May 2006; p. 4. [Google Scholar]
- Zhou, G.; Zhao, Y.; Guo, F.; Xu, W. A smart high accuracy silicon piezoresistive pressure sensor temperature compensation system. Sensors 2014, 14, 12174–12190. [Google Scholar] [CrossRef] [PubMed]
- Yan, Z.-S.; Lin, W.-H.; Liu, C.-H. Measurement of the thermal elongation of high speed spindles in real time using a cat’s eye reflector based optical sensor. Sens. Actuators A Phys. 2015, 221, 154–160. [Google Scholar] [CrossRef]
- Lee, H.-W.; Liu, C.-H. High precision optical sensors for real-time on-line measurement of straightness and angular errors for smart manufacturing. Smart Sci. 2016, 4, 134–141. [Google Scholar] [CrossRef]
- Lee, H.-W.; Chiu, T.-P.; Liu, C.-H. A 3D optical sensor using optical axis deviation method for rotational errors. Sens. Mater. 2016, 28, 14. [Google Scholar]
- Ashhab, M.D.S.; Al-Salaymeh, A. Optimization of hot-wire thermal flow sensor based on a neural net model. Appl. Therm. Eng. 2006, 26, 948–955. [Google Scholar] [CrossRef]
- Rivera, J.; Carrillo, M.; Chacón, M.; Herrera, G.; Bojorquez, G. Self-calibration and optimal response in intelligent sensors design based on artificial neural networks. Sensors 2007, 7, 1509–1529. [Google Scholar] [CrossRef]
- Salvado, J.; Espírito-Santo, A.; Calado, M. An intelligent sensor array distributed system for vibration analysis and acoustic noise characterization of a linear switched reluctance actuator. Sensors 2012, 12, 7614–7633. [Google Scholar] [CrossRef] [PubMed]
- Lee, S.-H.; Yang, C.-S. An intelligent power monitoring and analysis system for distributed smart plugs sensor networks. Int. J. Distrib. Sens. Netw. 2017, 13. [Google Scholar] [CrossRef] [Green Version]
Various Learning Algorithms (67 Literature) | Diagnosis and Detection of Mechanical Components (52 Literature) | AI Technique Applications in Smart Machine Tools (44 Literature) |
---|---|---|
DL (51): RNN [4,5,6,7,8,9,10,11,12,13,14], CNN [15,16,17,18,19,20,21,22,23,24,25,26,27,28], DNN [29,30,31,32,33], RBM [34,35,36], others [1,2,3,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51] | Bearings (24): [68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91] | Intelligent Manufacturing (22): [120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141] |
UL (5): [52,53,54,55,56] | Gear boxes (8): [92,93,94,95,96,97,98,99] | Prognosis of Mechanical Components (9): [142,143,144,145,146,147,148,149,150] |
ML (5): [57,58,59,60,61] | Aircraft (4): [100,101,102,103] | Smart Sensors (13): [151,152,153,154,155,156,157,158,159,160,161,162,163] |
SL (2): [62,63] | Rotors (3): [104,105,106] | |
RL (2): [64,65,66,67] | Others (13): [107,108,109,110,111,112,113,114,115,116,117,118,119] |
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Chang, C.-W.; Lee, H.-W.; Liu, C.-H. A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools. Inventions 2018, 3, 41. https://doi.org/10.3390/inventions3030041
Chang C-W, Lee H-W, Liu C-H. A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools. Inventions. 2018; 3(3):41. https://doi.org/10.3390/inventions3030041
Chicago/Turabian StyleChang, Chih-Wen, Hau-Wei Lee, and Chein-Hung Liu. 2018. "A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools" Inventions 3, no. 3: 41. https://doi.org/10.3390/inventions3030041