The World Bank. Manufacturing, value added (% of GDP), 2019.
A Kumar. From mass customization to mass personalization: A strategic transformation. International Journal of Flexible Manufacturing Systems, 2007, 19(4): 533-547.
Article
Google Scholar
S J Hu. Evolving paradigms of manufacturing: From mass production to mass customization and personalization. Procedia CIRP, 2013, 7: 3-8.
Article
Google Scholar
L Monostori, B Kádár, T Bauernhansl, et al. Cyber-physical systems in manufacturing. CIRP Annals, 2016, 65(2): 621-641.
Article
Google Scholar
R Y Zhong, X Xu, E Klotz, et al. Intelligent manufacturing in the context of Industry 4.0: A review. Engineering, 2017, 3(5): 616-630.
Article
Google Scholar
R Gao, L Wang, M Helu, et al. Big data analytics for smart factories of the future. CIRP Annals, 2020, 69(2): 668-692.
Article
Google Scholar
R Gao, L Wang, R Teti, et al. Cloud-enabled prognosis for manufacturing. CIRP Annals, 2015, 64(2): 749-772.
Article
Google Scholar
A Kusiak. Smart manufacturing must embrace big data. Nature News, 2017, 544(7648): 23.
Article
Google Scholar
F Tao, Q Qi, A Liu, et al. Data-driven smart manufacturing. Journal of Manufacturing Systems, 2018, 48: 157-169.
Article
Google Scholar
Y LeCun, Y Bengio, G Hinton. Deep learning. Nature, 2015, 521(7553): 436-444.
Article
Google Scholar
M Sharp, R Ak, T Hedberg Jr. A survey of the advancing use and development of machine learning in smart manufacturing. Journal of Manufacturing Systems, 2018, 48: 170-179.
Article
Google Scholar
H Yang, S Kumara, S T Bukkapatnam, et al. The internet of things for smart manufacturing: A review. IISE Transactions, 2019, 51(11): 1190-1216.
Article
Google Scholar
P Wang, R Gao, Z Fan. Cloud computing for cloud manufacturing: Benefits and limitations. Journal of Manufacturing Science and Engineering, 2015, 137(4).
Article
Google Scholar
A Cano. A survey on graphic processing unit computing for large‐scale data mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018, 8(1): e1232.
Google Scholar
L Wang, R Gao, J Váncza, et al. Symbiotic human-robot collaborative assembly. CIRP Annals, 2019, 68(2): 701-726.
Article
Google Scholar
C Wang, X P Tan, S B Tor, et al. Machine learning in additive manufacturing: State-of-the-art and perspectives. Additive Manufacturing, 2020: 101538.
Article
Google Scholar
S Khan, T Yairi. A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 2018, 107: 241-265.
Article
Google Scholar
J Cao, E Brinksmeier, M Fu, et al. Manufacturing of advanced smart tooling for metal forming. CIRP Annals, 2019, 68(2): 605-628.
Article
Google Scholar
Z Zhao, T Li, J Wu, et al. Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA Transactions, 2020, 107: 224-255.
Article
Google Scholar
G Qian, S Lu, D Pan, et al. Edge computing: A promising framework for real-time fault diagnosis and dynamic control of rotating machines using multi-sensor data. IEEE Sensors Journal, 2019, 19(11): 4211-4220.
Article
Google Scholar
L Zhang, J Lin, B Liu, et al. A review on deep learning applications in prognostics and health management. IEEE Access, 2019, 7: 162415-162438.
Article
Google Scholar
J Wang, Y Ma, L Zhang, et al. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 2018, 48: 144-156.
Article
Google Scholar
R Zhao, R Yan, Z Chen, et al. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 2019, 115: 213-237.
Article
Google Scholar
D Kozjek, D Kralj, P Butala. Interpretative identification of the faulty conditions in a cyclic manufacturing process. Journal of Manufacturing Systems, 2017, 43: 214-224.
Article
Google Scholar
W Samek, T Wiegand, K R Müller. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. 2017, arXiv preprint arXiv:1708.08296.
A Freitas, E Curry. Big data curation. In: New horizons for a data-driven economy. Springer, Cham, 2016: 87-118.
Chapter
Google Scholar
R Roscher, B Bohn, M F Duarte, et al. Explainable machine learning for scientific insights and discoveries. IEEE Access, 2020, 8: 42200-42216.
Article
Google Scholar
Y Wang, X Sun, J Fleischer. When deep denoising meets iterative phase retrieval. International Conference on Machine Learning, 2020: 10007–10017.
I Goodfellow, J Pouget-Abadie, M Mirza, et al. Generative adversarial nets. Neural Information Processing Systems, 2014, 3: 2672-2680.
J Long, E Shelhamer, T Darrell. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
Google Scholar
S Bach, A Binder, G Montavon, et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One, 2015, 10(7): e0130140.
Article
Google Scholar
D Bahdanau, K Cho, B Y Engio. Neural machine translation by jointly learning to align and translate. 2014: arXiv preprint arXiv:1409.0473.
M Raissi, P Perdikaris, G E Karniadakis. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707.
Article
MathSciNet
MATH
Google Scholar
General Electric Intelligent Platforms. The rise of industrial big data. silo.tips/download/the-rise-of-industrial-big-data-2, 2012.
L Song, F Wang, S Li, et al. Phase congruency melt pool edge extraction for laser additive manufacturing. Journal of Materials Processing Technology, 2017, 250: 261-269.
Article
Google Scholar
R Yan, R Gao. A nonlinear noise reduction approach to vibration analysis for bearing health diagnosis. Journal of Computational and Nonlinear Dynamics, 2012, 7(2).
Article
Google Scholar
S Liu, R Gao, D John, et al. Tissue artifact removal from respiratory signals based on empirical mode decomposition. Annals of Biomedical Engineering, 2013, 41(5): 1003-1015.
Article
Google Scholar
A M Wink, J B Roerdink. Denoising functional MR images: A comparison of wavelet denoising and Gaussian smoothing. IEEE Transactions on Medical Imaging, 2004, 23(3): 374-387.
Article
Google Scholar
J Gao, H Sultan, J Hu, et al. Denoising nonlinear time series by adaptive filtering and wavelet shrinkage: a comparison. IEEE Signal Processing Letters, 2009, 17(3): 237-240.
Google Scholar
B Holm-Hansen, R Gao, L Zhang. Customized wavelet for bearing defect detection. Journal of Dynamic Systems, Measurement, and Control, 2004, 126(4): 740-745.
Article
Google Scholar
J Collins, C Chow, T Imhoff. Stochastic resonance without tuning. Nature, 1995, 376(6537): 236-238.
Article
Google Scholar
R Zhao, R Yan, R Gao. Dual-scale cascaded adaptive stochastic resonance for rotary machine health monitoring. Journal of Manufacturing Systems, 2013, 32(4): 529-535.
Article
Google Scholar
C Wang, F A Cheikh, M Kaaniche, et al. Variational based smoke removal in laparoscopic images. Biomedical Engineering Online, 2018, 17(1): 1-18.
Article
Google Scholar
C Tian, L Fei, W Zheng, et al. Deep learning on image denoising: An overview. Neural Networks, 2020, https://doi.org/10.1016/j.neunet.2020.07.025.
Article
Google Scholar
M Elad, M Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745.
Article
MathSciNet
Google Scholar
S Diamond, V Sitzmann, F Heide, et al. Unrolled optimization with deep priors. 2017: arXiv preprint arXiv:1705.08041.
P Hand, O Leong, V Voroninski. Phase retrieval under a generative prior. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018: 9154–9164.
F Wan, G Guo, C Zhang, et al. Outlier detection for monitoring data using stacked autoencoder. IEEE Access, 2019, 7: 173827-173837.
Article
Google Scholar
W Lin, C Tsai. Missing value imputation: A review and analysis of the literature (2006–2017). Artificial Intelligence Review, 2020, 53(2): 1487-1509.
Article
Google Scholar
H Wang, M J Bah, M Hammad. Progress in outlier detection techniques: A survey. IEEE Access, 2019, 7: 107964-108000.
Article
Google Scholar
M Ahsan, M Mashuri, H Kuswanto, et al. Outlier detection using PCA mix based T 2 control chart for continuous and categorical data. Communications in Statistics-Simulation and Computation, 2019: 1-28.
Article
Google Scholar
J Ahn, M H Lee, J A Lee. Distance-based outlier detection for high dimension, low sample size data. Journal of Applied Statistics, 2019, 46(1): 13-29.
Article
MathSciNet
Google Scholar
G Bhattacharya, K Ghosh, A S Chowdhury. Outlier detection using neighborhood rank difference. Pattern Recognition Letters, 2015, 60: 24-31.
Article
Google Scholar
G Gan, M K P Ng. K-means clustering with outlier removal. Pattern Recognition Letters, 2017, 90: 8-14.
Article
Google Scholar
Y Xia, X Cao, F Wen, et al. Learning discriminative reconstructions for unsupervised outlier removal. Proceedings of the IEEE International Conference on Computer Vision, 2015: 1511–1519.
B Lakshminarayanan, A Pritzel, C Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6405–6416.
Y Gal, Z Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. International Conference on Machine Learning, 2016: 1050-1059.
Google Scholar
A Kendall, Y Gal. What uncertainties do we need in Bayesian deep learning for computer vision? Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 5580–5590.
J Linmans, J van der Laak, G Litjens. Efficient out-of-distribution detection in digital pathology using multi-head convolutional neural networks. Medical Imaging with Deep Learning, 2020: 465–478.
S Liang, Y Li, R Srikant. Enhancing the reliability of out-of-distribution image detection in neural networks. International Conference on Learning Representations, 2018: 1-15.
K Lee, K Lee, H Lee, et al. A simple unified framework for detecting out-of-distribution samples and adversarial attacks. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018: 7167–7177.
J Ma, J C Cheng, F Jiang, et al. A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data. Energy and Buildings, 2020, 216: 109941.
Article
Google Scholar
T Ouyang, X Zha, L Qin. A combined multivariate model for wind power prediction. Energy Conversion and Management, 2017, 144: 361-373.
Article
Google Scholar
A A Kasam, B D Lee, C J Paredis. Statistical methods for interpolating missing meteorological data for use in building simulation. Building Simulation, 2014, 7(5): 455-465.
Article
Google Scholar
Z Che, S Purushotham, K Cho, et al. Recurrent neural networks for multivariate time series with missing values. Scientific Reports, 2018, 8(1): 1-12.
Google Scholar
W Cao, D Wang, J Li, et al. BRITS: bidirectional recurrent imputation for time series. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 2018: 6776–6786.
Y Zhuang, R Ke, Y Wang. Innovative method for traffic data imputation based on convolutional neural network. IET Intelligent Transport Systems, 2018, 13(4): 605-613.
Article
Google Scholar
D Ulyanov, A Vedaldi, V Lempitsky. Deep image prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9446–9454.
Y Zhang, X Li, L Gao, et al. Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning. Journal of Manufacturing Systems, 2018, 48: 34-50.
Article
Google Scholar
P Santos, J Maudes, B A ustillo. Identifying maximum imbalance in datasets for fault diagnosis of gearboxes. Journal of Intelligent Manufacturing, 2018, 29(2): 333-351.
Article
Google Scholar
R Yan, F Shen, C Sun, et al. Knowledge transfer for rotary machine fault diagnosis. IEEE Sensors Journal, 2019, 20(15): 8374-8393.
Article
Google Scholar
C Li, S Zhang, Y Qin, et al. A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, 2020, 407: 121-135.
Article
Google Scholar
B Yang, Y Lei, F Jia, et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings. Mechanical Systems and Signal Processing, 2019, 122: 692-706.
Article
Google Scholar
S Xing, Y Lei, S Wang, et al. Distribution-invariant deep belief network for intelligent fault diagnosis of machines under new working conditions. IEEE Transactions on Industrial Electronics, 2020, 68(3): 2617-2625.
Article
Google Scholar
N V Chawla, K W Bowyer, L O Hall, et al. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 2002, 16: 321-357.
Article
MATH
Google Scholar
D P Kingma, M Welling. Auto-encoding variational bayes.2013: arXiv preprint arXiv:1312.6114.
M Grasso, A G Demir, B Previtali, et al. In situ monitoring of selective laser melting of zinc powder via infrared imaging of the process plume. Robotics and Computer-Integrated Manufacturing, 2018, 49: 229-239.
Article
Google Scholar
S Clijsters, T Craeghs, S Buls, et al. In situ quality control of the selective laser melting process using a high-speed, real-time melt pool monitoring system. The International Journal of Advanced Manufacturing Technology, 2014, 75(5-8): 1089-1101.
Article
Google Scholar
O Ronneberger, P Fischer, T Brox. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 234–241.
K He, G Gkioxari, P Dollár, et al. Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961–2969.
T Lei, Q Zhang, D Xue, et al. End-to-end change detection using a symmetric fully convolutional network for landslide mapping. ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019: 3027–3031.
K He, X Zhang, S Ren, et al. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.
D W Otter, J R Medina, J K Kalita. A survey of the usages of deep learning for natural language processing. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(2): 604-624.
T Mikolov, K Chen, G Corrado, et al. Efficient estimation of word representations in vector space. 2013: arXiv preprint arXiv:1301.3781.
T Sexton, M P Brundage, M Hoffman, et al. Hybrid datafication of maintenance logs from ai-assisted human tags. 2017 IEEE International Conference on Big Data, 2017: 1769–1777.
A Thomas, S Sangeetha. Deep learning architectures for named entity recognition: A survey. In: Advanced computing and intelligent engineering, 2020: 215–225.
A Vaswani, N Shazeer, N Parmar, et al. Attention is all you need. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000–6010.
J Devlin, M W Chang, K Lee, et al. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, 2019: 4171–4180.
T Chen, J Zhu, Z Zeng, et al. Compressor fault diagnosis knowledge: A benchmark dataset for knowledge extraction from maintenance log sheets based on sequence labeling. IEEE Access, 2021: 59394–59405.
K Simonyan, A Vedaldi, A Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. 2013: arXiv preprint arXiv:1312.6034.
B Dickson. Deep learning doesn’t need to be a black box. https://bdtechtalks.com/2021/01/11/concept-whitening-interpretable-neural-networks/.
M D Zeiler, R Fergus. Visualizing and understanding convolutional networks. European Conference on Computer Vision, 2014: 818–833.
Z Qin, F Yu, C Liu, et al. How convolutional neural network see the world-A survey of convolutional neural network visualization methods. 2018: arXiv preprint arXiv:1804.11191.
M T Luong, H Pham, C D Manning. Effective approaches to attention-based neural machine translation. 2015: arXiv preprint arXiv:1508.04025.
A Karpatne, W Watkins, J Read, et al. How can physics inform deep learning methods in scientific problems? Recent Progress and Future Prospects. 31st Conference on Neural Information Processing Systems (NeurIPS), 2017: 1–5.
T J Choi, N Subrahmanya, H Li, et al. Generalized practical models of cylindrical plunge grinding processes. International Journal of Machine Tools and Manufacture, 2008, 48(1): 61-72.
Article
Google Scholar
G Xiao, S Malkin. On-line optimization for internal plunge grinding. CIRP Annals, 1996, 45(1): 287-292.
Article
Google Scholar
A Mansour, H Abdalla. Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN32) in dry condition. Journal of Materials Processing Technology, 2002, 124(1-2): 183-191.
Article
Google Scholar
Q Tian, S Guo, Y Guo. A physics-driven deep learning model for process-porosity causal relationship and porosity prediction with interpretability in laser metal deposition. CIRP Annals, 2020, 69(1): 205-208.
Article
Google Scholar
P C Paris, F A Erdogan. Critical analysis of crack propagation laws. Journal of Basic Engineering, 1963, D85(4): 528-534.
Article
Google Scholar
R G Nascimento, F A Viana. Fleet prognosis with physics-informed recurrent neural networks. 2019: arXiv preprint arXiv:1901.05512.
J Wang, Y Li, R Zhao, et al. Physics guided neural network for machining tool wear prediction. Journal of Manufacturing Systems, 2020, 57: 298-310.
Article
Google Scholar
X Jia, J Willard, A Karpatne, et al. Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles. Proceedings of the 2019 SIAM International Conference on Data Mining, 2019: 558–566.
A Karpatne, W Watkins, J Read, et al. Physics-guided neural networks (pgnn): An application in lake temperature modeling. 2017: arXiv preprint arXiv:1710.11431.
ISO 17359: Condition monitoring and diagnostics of machines – General guidelines.
H Miao, Z Zhao, C Sun, et al. A U-Net-Based approach for tool wear area detection and identification. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-10.
Google Scholar
S M chuster, K K Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681.
Article
Google Scholar
J Zhang, P Wang, R X Gao. Attention mechanism-incorporated deep learning for AM part quality prediction. Procedia CIRP, 2020, 93: 96-101.
Article
Google Scholar
Y O Lee, J Jo, J Hwang. Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection. 2017 IEEE International Conference on Big Data, 2017: 3248-3253.
Google Scholar
S Shao, P Wang, R Yan. Generative adversarial networks for data augmentation in machine fault diagnosis. Computers in Industry, 2019, 106: 85-93.
Article
Google Scholar
Z Wang, J Wang, Y Wang. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing, 2018, 310: 213-222.
Article
Google Scholar
C Cooper, J Zhang, R X Gao, et al. Anomaly detection in milling tools using acoustic signals and generative adversarial networks. Procedia Manufacturing, 2020, 48: 372-378.
Article
Google Scholar
L Scime, D Siddel, S Baird, et al. Layer-wise anomaly detection and classification for powder bed additive manufacturing processes: A machine-agnostic algorithm for real-time pixel-wise semantic segmentation. Additive Manufacturing, 2020, 36: 101453.
Article
Google Scholar
Z Jin, Z Z hang, J Ott, et al. Precise localization and semantic segmentation detection of printing conditions in fused filament fabrication technologies using machine learning. Additive Manufacturing, 2021, 37: 101696.
Article
Google Scholar
H Wu, W Gao, X Xu. Solder joint recognition using mask R-CNN method. IEEE Transactions on Components, Packaging and Manufacturing Technology, 2019, 10(3): 525-530.
Article
Google Scholar
Z Huang, W Xu, K Yu. Bidirectional LSTM-CRF models for sequence tagging. 2015: arXiv preprint arXiv:1508.01991.
J Grezmak, J Zhang, P Wang, et al. Interpretable convolutional neural network through layer-wise relevance propagation for machine fault diagnosis. IEEE Sensors Journal, 2019, 20(6): 3172-3181.
Article
Google Scholar
J Grezmak, P Wang, C Sun, et al. Explainable convolutional neural network for gearbox fault diagnosis. Procedia CIRP, 2019, 80: 476-481.
Article
Google Scholar
M Lee, J Jeon, H Lee. Explainable AI for domain experts: A post Hoc analysis of deep learning for defect classification of TFT–LCD panels. Journal of Intelligent Manufacturing, 2021: 1-13.
Article
Google Scholar
X Li, W Zhang, Q Ding. Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism. Signal Processing, 2019, 161: 136-154.
Article
Google Scholar
Z B Yang, J P Zhang, Z B Zhao, et al. Interpreting network knowledge with attention mechanism for bearing fault diagnosis. Applied Soft Computing, 2020, 97: 106829.
Article
Google Scholar
T Li, Z Zhao, C Sun, et al. WaveletKernelNet: An interpretable deep neural network for industrial intelligent diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021: 1–11.
R Gao, R Yan. Wavelets: Theory and applications for manufacturing. Springer Science & Business Media, 2010.
Google Scholar
S A Khan, A E Prosvirin, J M Kim. Towards bearing health prognosis using generative adversarial networks: Modeling bearing degradation. 2018 International Conference on Advancements in Computational Sciences (ICACS), 2018: 1–6.
G Hou, S Xu, N Zhou, et al. Remaining useful life estimation using deep convolutional generative adversarial networks based on an autoencoder scheme. Computational Intelligence and Neuroscience, 2020.
Article
Google Scholar
Z Chen, M Wu, R Zhao, et al. Machine remaining useful life prediction via an attention-based deep learning approach. IEEE Transactions on Industrial Electronics, 2020, 68(3): 2521-2531.
Article
Google Scholar
Y Chen, G Peng, Z Zhu, et al. A novel deep learning method based on attention mechanism for bearing remaining useful life prediction. Applied Soft Computing, 2020, 86: 105919.
Article
Google Scholar
M Fujishima, K Ohno, S Nishikawa, et al. Study of sensing technologies for machine tools. CIRP Journal of Manufacturing Science and Technology, 2016, 14, 71-75.
Article
Google Scholar
H Wang, D Y Yeung. Towards Bayesian deep learning: A framework and some existing methods. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3395-3408.
Article
Google Scholar
S Depeweg, J M Hernandez-Lobato, F Doshi-Velez, et al. Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. International Conference on Machine Learning, 2018: 1184–1193.
R F Barber, E J Candès. Controlling the false discovery rate via knockoffs. Annals of Statistics, 2015, 43(5): 2055-2085.
Article
MathSciNet
MATH
Google Scholar