Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis

Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

  • Jianjing Zhang ,
  • Robert X. Gao
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  • Department of Mechanical and Aerospace Engineering, Case Wester Reserve University, Cleveland, OH 44106-7222, USA

收稿日期: 2020-12-10

  修回日期: 2021-05-25

  网络出版日期: 2021-12-21

Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing

  • Jianjing Zhang ,
  • Robert X. Gao
Expand
  • Department of Mechanical and Aerospace Engineering, Case Wester Reserve University, Cleveland, OH 44106-7222, USA

Received date: 2020-12-10

  Revised date: 2021-05-25

  Online published: 2021-12-21

摘要

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the pblack-boxq nature of DL towards model transparency.

本文引用格式

Jianjing Zhang , Robert X. Gao . Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(3) : 71 -71 . DOI: 10.1186/s10033-021-00587-y

Abstract

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments, smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art. While the widespread application of deep learning (DL) has opened up new opportunities to accomplish the goal, data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications. This has motivated research on two fronts: data curation, which aims to provide quality data as input for meaningful DL-based analysis, and model interpretation, which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users. This paper summarizes several key techniques in data curation where breakthroughs in data denoising, outlier detection, imputation, balancing, and semantic annotation have demonstrated the effectiveness in information extraction from noisy, incomplete, insufficient, and/or unannotated data. Also highlighted are model interpretation methods that address the pblack-boxq nature of DL towards model transparency.

参考文献

[1] The World Bank. Manufacturing, value added (% of GDP), 2019.
[2] A Kumar. From mass customization to mass personalization: A strategic transformation. International Journal of Flexible Manufacturing Systems, 2007, 19(4): 533-547.
[3] S J Hu. Evolving paradigms of manufacturing: From mass production to mass customization and personalization. Procedia CIRP, 2013, 7: 3-8.
[4] L Monostori, B Kádár, T Bauernhansl, et al. Cyber-physical systems in manufacturing. CIRP Annals, 2016, 65(2): 621-641.
[5] 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.
[6] R Gao, L Wang, M Helu, et al. Big data analytics for smart factories of the future. CIRP Annals, 2020, 69(2): 668-692.
[7] R Gao, L Wang, R Teti, et al. Cloud-enabled prognosis for manufacturing. CIRP Annals, 2015, 64(2): 749-772.
[8] A Kusiak. Smart manufacturing must embrace big data. Nature News, 2017, 544(7648): 23.
[9] F Tao, Q Qi, A Liu, et al. Data-driven smart manufacturing. Journal of Manufacturing Systems, 2018, 48: 157-169.
[10] Y LeCun, Y Bengio, G Hinton. Deep learning. Nature, 2015, 521(7553): 436-444.
[11] 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.
[12] 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.
[13] P Wang, R Gao, Z Fan. Cloud computing for cloud manufacturing: Benefits and limitations. Journal of Manufacturing Science and Engineering, 2015, 137(4).
[14] 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.
[15] L Wang, R Gao, J Váncza, et al. Symbiotic human-robot collaborative assembly. CIRP Annals, 2019, 68(2): 701-726.
[16] 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.
[17] 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.
[18] J Cao, E Brinksmeier, M Fu, et al. Manufacturing of advanced smart tooling for metal forming. CIRP Annals, 2019, 68(2): 605-628.
[19] 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.
[20] 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.
[21] 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.
[22] J Wang, Y Ma, L Zhang, et al. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 2018, 48: 144-156.
[23] 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.
[24] 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.
[25] W Samek, T Wiegand, K R Müller. Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. 2017, arXiv preprint arXiv: 1708.08296.
[26] A Freitas, E Curry. Big data curation. In: New horizons for a data-driven economy. Springer, Cham, 2016: 87-118.
[27] R Roscher, B Bohn, M F Duarte, et al. Explainable machine learning for scientific insights and discoveries. IEEE Access, 2020, 8: 42200-42216.
[28] Y Wang, X Sun, J Fleischer. When deep denoising meets iterative phase retrieval. International Conference on Machine Learning, 2020: 10007–10017.
[29] I Goodfellow, J Pouget-Abadie, M Mirza, et al. Generative adversarial nets. Neural Information Processing Systems, 2014, 3: 2672-2680.
[30] 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.
[31] 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.
[32] D Bahdanau, K Cho, B Y Engio. Neural machine translation by jointly learning to align and translate. 2014: arXiv preprint arXiv: 1409.0473.
[33] 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.
[34] General Electric Intelligent Platforms. The rise of industrial big data. silo. tips/download/the-rise-of-industrial-big-data-2, 2012.
[35] 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.
[36] 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).
[37] 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.
[38] 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.
[39] 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.
[40] 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.
[41] J Collins, C Chow, T Imhoff. Stochastic resonance without tuning. Nature, 1995, 376(6537): 236-238.
[42] 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.
[43] C Wang, F A Cheikh, M Kaaniche, et al. Variational based smoke removal in laparoscopic images. Biomedical Engineering Online, 2018, 17(1): 1-18.
[44] 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.
[45] M Elad, M Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 2006, 15(12): 3736-3745.
[46] S Diamond, V Sitzmann, F Heide, et al. Unrolled optimization with deep priors. 2017: arXiv preprint arXiv: 1705.08041.
[47] 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.
[48] F Wan, G Guo, C Zhang, et al. Outlier detection for monitoring data using stacked autoencoder. IEEE Access, 2019, 7: 173827-173837.
[49] W Lin, C Tsai. Missing value imputation: A review and analysis of the literature (2006–2017). Artificial Intelligence Review, 2020, 53(2): 1487-1509.
[50] H Wang, M J Bah, M Hammad. Progress in outlier detection techniques: A survey. IEEE Access, 2019, 7: 107964-108000.
[51] 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.
[52] 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.
[53] G Bhattacharya, K Ghosh, A S Chowdhury. Outlier detection using neighborhood rank difference. Pattern Recognition Letters, 2015, 60: 24-31.
[54] G Gan, M K P Ng. K-means clustering with outlier removal. Pattern Recognition Letters, 2017, 90: 8-14.
[55] 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.
[56] 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.
[57] Y Gal, Z Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. International Conference on Machine Learning, 2016: 1050-1059.
[58] 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.
[59] 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.
[60] 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.
[61] 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.
[62] 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.
[63] T Ouyang, X Zha, L Qin. A combined multivariate model for wind power prediction. Energy Conversion and Management, 2017, 144: 361-373.
[64] 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.
[65] 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.
[66] 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.
[67] 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.
[68] D Ulyanov, A Vedaldi, V Lempitsky. Deep image prior. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9446–9454.
[69] 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.
[70] 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.
[71] R Yan, F Shen, C Sun, et al. Knowledge transfer for rotary machine fault diagnosis. IEEE Sensors Journal, 2019, 20(15): 8374-8393.
[72] C Li, S Zhang, Y Qin, et al. A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, 2020, 407: 121-135.
[73] 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.
[74] 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.
[75] 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.
[76] D P Kingma, M Welling. Auto-encoding variational bayes. 2013: arXiv preprint arXiv: 1312.6114.
[77] 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.
[78] 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.
[79] 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.
[80] K He, G Gkioxari, P Dollár, et al. Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961–2969.
[81] 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.
[82] 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.
[83] 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.
[84] T Mikolov, K Chen, G Corrado, et al. Efficient estimation of word representations in vector space. 2013: arXiv preprint arXiv: 1301.3781.
[85] 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.
[86] A Thomas, S Sangeetha. Deep learning architectures for named entity recognition: A survey. In: Advanced computing and intelligent engineering, 2020: 215–225.
[87] 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.
[88] 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.
[89] 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.
[90] K Simonyan, A Vedaldi, A Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. 2013: arXiv preprint arXiv: 1312.6034.
[91] B Dickson. Deep learning doesn't need to be a black box. https://bdtechtalks.com/2021/01/11/concept-whitening-interpretable-neural-networks/.
[92] M D Zeiler, R Fergus. Visualizing and understanding convolutional networks. European Conference on Computer Vision, 2014: 818–833.
[93] 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.
[94] M T Luong, H Pham, C D Manning. Effective approaches to attention-based neural machine translation. 2015: arXiv preprint arXiv: 1508.04025.
[95] 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.
[96] 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.
[97] G Xiao, S Malkin. On-line optimization for internal plunge grinding. CIRP Annals, 1996, 45(1): 287-292.
[98] 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.
[99] 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.
[100] P C Paris, F A Erdogan. Critical analysis of crack propagation laws. Journal of Basic Engineering, 1963, D85(4): 528-534.
[101] R G Nascimento, F A Viana. Fleet prognosis with physics-informed recurrent neural networks. 2019: arXiv preprint arXiv: 1901.05512.
[102] 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.
[103] 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.
[104] 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.
[105] ISO 17359: Condition monitoring and diagnostics of machines – General guidelines.
[106] 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.
[107] S M chuster, K K Paliwal. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 1997, 45(11): 2673-2681.
[108] J Zhang, P Wang, R X Gao. Attention mechanism-incorporated deep learning for AM part quality prediction. Procedia CIRP, 2020, 93: 96-101.
[109] 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.
[110] S Shao, P Wang, R Yan. Generative adversarial networks for data augmentation in machine fault diagnosis. Computers in Industry, 2019, 106: 85-93.
[111] 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.
[112] 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.
[113] 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.
[114] 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.
[115] 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.
[116] Z Huang, W Xu, K Yu. Bidirectional LSTM-CRF models for sequence tagging. 2015: arXiv preprint arXiv: 1508.01991.
[117] 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.
[118] J Grezmak, P Wang, C Sun, et al. Explainable convolutional neural network for gearbox fault diagnosis. Procedia CIRP, 2019, 80: 476-481.
[119] 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.
[120] 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.
[121] 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.
[122] 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.
[123] R Gao, R Yan. Wavelets: Theory and applications for manufacturing. Springer Science & Business Media, 2010.
[124] 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.
[125] 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.
[126] 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.
[127] 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.
[128] 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.
[129] 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.
[130] 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.
[131] R F Barber, E J Candès. Controlling the false discovery rate via knockoffs. Annals of Statistics, 2015, 43(5): 2055-2085.
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