├── LICENSE ├── README.md ├── doc ├── 2020 │ ├── 0413.md │ └── 0420.md ├── 0413.md ├── FD.md ├── RULP.md └── TD.md └── notes └── papernotes1.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Cuixiaolong 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep-learning-in-PHM 2 | Deep learning in PHM，Deep learning in fault diagnosis，Deep learning in remaining useful life prediction 3 | 4 | The purpose of this repository is to collect the application research of deep learning in PHM field, collect and organize the open-source algorithm resources, and provide a platform for researchers to learn and communicate. 5 | 6 | ## papers 7 | 8 | ### Weekly paper express 9 | - 2020 10 | 11 | [0413-0419](/doc/2020/0413.md) | [0420-0426](/doc/2020/0420.md) 12 | 13 | ### review papers 14 | - Dalzochio, J., et al., Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Computers in Industry, 2020. 123: p. 103298.[link](https://doi.org/10.1016/j.compind.2020.103298) 15 | - Zhao, Z., et al., Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA Transactions, 2020.[link](https://doi.org/10.1016/j.isatra.2020.08.010) 16 | - Jiao, J., et al., A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 2020.[link](https://www.sciencedirect.com/science/article/pii/S092523122031225X) 17 | - Singh, J., et al., A systematic review of machine learning algorithms for PHM of rolling element bearings: fundamentals, concepts, and applications. Measurement Science and Technology, 2020.[link](https://iopscience.iop.org/article/10.1088/1361-6501/ab8df9) 18 | - Liu, Z. and L. Zhang, A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings. Measurement, 2020. 149: p. 107002.[link](https://doi.org/10.1016/j.measurement.2019.107002) 19 | - Wang, P. and R.X. Gao, Transfer learning for enhanced machine fault diagnosis in manufacturing. CIRP Annals, 2020.[link](https://doi.org/10.1016/j.cirp.2020.04.074) 20 | - Li, C., et al., A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing, 2020.[link](https://doi.org/10.1016/j.neucom.2020.04.045) 21 | - LeCun, Y., Y. Bengio and G. Hinton, Deep learning. Nature, 2015. 521: p. 436 22 | EP -.[link](https://www.nature.com/articles/nature14539) 23 | - Jiao, J., et al., A comprehensive review on convolutional neural network in machine fault diagnosis. 2020.[link](https://arxiv.org/abs/2002.07605) 24 | - Lei, Y., et al., Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 2020. 138: p. 106587.[link](https://www.sciencedirect.com/science/article/pii/S0888327019308088?via%3Dihub) 25 | - Zhao, R., et al., Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 2019. 115: p. 213-237.[link](https://www.sciencedirect.com/science/article/pii/S0888327018303108) 26 | - Khan, S. and T. Yairi, A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 2018. 107: p. 241-265.[link](https://www.sciencedirect.com/science/article/pii/S0888327017306064) 27 | - Hoang, D. and H. Kang, A survey on Deep Learning based bearing fault diagnosis. Neurocomputing, 2019. 335: p. 327-335.[link](https://www.sciencedirect.com/science/article/pii/S0925231218312657) 28 | - Liu, R., et al., Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 2018. 108: p. 33-47.[link](https://www.sciencedirect.com/science/article/pii/S0888327018300748) 29 | - Lee, J., et al., Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 2014. 42(1): p. 314-334.[link](https://www.sciencedirect.com/science/article/pii/S0888327013002860) 30 | - Chen, X., et al., Basic research on machinery fault diagnostics: Past, present, and future trends. Frontiers of Mechanical Engineering, 2018. 13(2): p. 264-291.[link](https://link.springer.com/article/10.1007%2Fs11465-018-0472-3) 31 | - El-Thalji, I. and E. Jantunen, A summary of fault modelling and predictive health monitoring of rolling element bearings. Mechanical Systems and Signal Processing, 2015. 60-61: p. 252-272.[link](https://www.sciencedirect.com/science/article/pii/S0888327015000813?via%3Dihub) 32 | - Cerrada, M., et al., A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 2018. 99: p. 169-196.[link](https://www.sciencedirect.com/science/article/pii/S0888327017303242) 33 | - Zhang, S., et al., Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review. arXiv preprint arXiv:1901.08247, 2019.[link](https://arxiv.org/abs/1901.08247) 34 | - Yan, R., et al., Knowledge Transfer for Rotary Machine Fault Diagnosis. IEEE Sensors Journal: p. 1-1.[link](https://ieeexplore.ieee.org/document/8880697) 35 | - Lei, Y., et al., Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 2018. 104: p. 799-834.[link](https://www.sciencedirect.com/science/article/pii/S0888327017305988) 36 | - Liu, W., et al., A survey of deep neural network architectures and their applications. Neurocomputing, 2017. 234: p. 11-26.[link](https://doi.org/10.1016/j.neucom.2016.12.038) 37 | 38 | 39 | 40 | 41 | ### Original research papers 42 | 43 | - [Fault diagnosis](./doc/FD.md) 44 | - [Trend prediction](./doc/TD.md) 45 | - [Remaining useful life prediction](./doc/RULP.md) 46 | 47 | 48 | ## Open source projects 49 | - [DL-based-Intelligent-Diagnosis-Benchmark](https://github.com/ZhaoZhibin/DL-based-Intelligent-Diagnosis-Benchmark) 50 | - [Algorithm recurrence of two highly cited papers](https://github.com/AiZhanghan/deep-learning-fault-diagnosis) 51 | - [to prediction the remain useful life of bearing based on 2012 PHM data](https://github.com/ddrrrr/projectRUL) 52 | - [Remaining Useful Life Prediction Using RNN/LSTM/GRU Neural Networks](https://github.com/lankuohsing/Remaining-Useful-Life-Prediction-RNN) 53 | - [Convolutional Recurrent Neural Networks for Remaining Useful Life Prediction in Mechanical Systems](https://github.com/nicolasoyharcabal/ConvRNN_for_RUL_estimation) 54 | - [NASA-Prognostics Algorithm Library](https://github.com/nasa/PrognosticsAlgorithmLibrary) 55 | 56 | 57 | ## Research teams 58 | - [Chen Xuefeng's team Xi'an Jiaotong University](http://gr.xjtu.edu.cn/web/chenxf/1) 59 | - [Lei Yaguo's team Xi'an Jiaotong University](http://gr.xjtu.edu.cn/web/yaguolei/research;jsessionid=BB8D3BEF8C8D431E9962790085F019EF) 60 | - [Wen Long's team China University of Geosciences (Wuhan)](http://jidian.cug.edu.cn/info/1131/3057.htm) 61 | - [Mingjian Zuo's team University of Alberta](https://sites.ualberta.ca/~mzuo/) 62 | - [ROBERT X. GAO Case Western Reserve University](https://engineering.case.edu/emae/node/286) 63 | - [Ming Liang University of Ottawa](http://by.genie.uottawa.ca/~liang/liang.htm) 64 | 65 | 66 | -------------------------------------------------------------------------------- /doc/0413.md: -------------------------------------------------------------------------------- 1 | # 0413-0419 2 | - [1]. Li, H., et al., A novel approach for predicting tool remaining useful life using limited data. Mechanical Systems and Signal Processing, 2020. 143: p. 106832. 3 | -------------------------------------------------------------------------------- /doc/2020/0413.md: -------------------------------------------------------------------------------- 1 | 2 | # Weekly paper express 0413-0419 3 | 4 | - [1]. Li, H., et al., A novel approach for predicting tool remaining useful life using limited data. Mechanical Systems and Signal Processing, 2020. 143: p. 106832.[link](https://doi.org/10.1016/j.ymssp.2020.106832) 5 | - [2]. Azamfar, M., et al., Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis. Mechanical Systems and Signal Processing, 2020. 144: p. 106861.[link](https://doi.org/10.1016/j.ymssp.2020.106861) 6 | - [3]. Li, X., X. Li and H. Ma, Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery. Mechanical Systems and Signal Processing, 2020. 143: p. 106825.[link](https://doi.org/10.1016/j.ymssp.2020.106825) 7 | - [4]. Gai, J., et al., A Parameter-Optimized DBN Using GOA and Its Application in Fault Diagnosis of Gearbox. Shock and Vibration, 2020. 2020: p. 1-11.[link](https://doi.org/10.1155/2020/4294095) 8 | - [5] Xu, Zhi, Gang Tang, and Mengfu He. "Peak-Based Mode Decomposition for Weak Fault Feature Enhancement and Detection of Rolling Element Bearing." Shock and Vibration 2020 (2020).[link](https://doi.org/10.1155/2020/8901794) 9 | 10 | 11 | [:back:BACK](../../README.md) 12 | -------------------------------------------------------------------------------- /doc/2020/0420.md: -------------------------------------------------------------------------------- 1 | # Weekly paper express 0420-0426 2 | 3 | 4 | - [1]. Karimi Pour, F., et al., Health-aware control design based on remaining useful life estimation for autonomous racing vehicle. ISA Transactions, 2020. 5 | [link](https://doi.org/10.1016/j.isatra.2020.03.032) 6 | 7 | 8 | 9 | 10 | [:back:返回](../../README.md) 11 | -------------------------------------------------------------------------------- /doc/FD.md: -------------------------------------------------------------------------------- 1 | # Fault Diagnosis 2 | 3 | ## Bearing fault diagnosis 4 | - Xin, L., et al., Modified Gaussian convolutional deep belief network and infrared thermal imaging for intelligent fault diagnosis of rotor-bearing system under time-varying speeds. Structural Health Monitoring, 2021: p. 147592172199895.[link](https://doi.org/10.1177/1475921721998957) 5 | - Chen, J., et al., Unsupervised Cross-domain Fault Diagnosis Using Feature Representation Alignment Networks for Rotating Machinery. IEEE/ASME Transactions on Mechatronics: p. 1-1.[link](https://ieeexplore.ieee.org/abstract/document/9301443) 6 | - T. Li, Z. Zhao, C. Sun, R. Yan and X. Chen, "Multi-receptive Field Graph Convolutional Networks for Machine Fault Diagnosis," in IEEE Transactions on Industrial Electronics, doi: 10.1109/TIE.2020.3040669.[link](https://ieeexplore.ieee.org/document/9280401) 7 | - Jin, Y., et al., Actual Bearing Compound Fault Diagnosis based on Active Learning and Decoupling Attentional Residual Network. Measurement, 2020: p. 108500.[link](https://doi.org/10.1016/j.measurement.2020.108500) 8 | - Tong, J., et al., A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing. Shock and Vibration, 2020. 2020: p. 1-12.[link](https://doi.org/10.1155/2020/8891905) 9 | - Mao, W., et al., A new deep auto-encoder method with fusing discriminant information for bearing fault diagnosis. Mechanical Systems and Signal Processing, 2021. 150: p. 107233.[link](https://doi.org/10.1016/j.ymssp.2020.107233) 10 | - Chen, Z., et al., A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks. Mechanical Systems and Signal Processing, 2020. 140: p. 106683.[link](https://doi.org/10.1016/j.ymssp.2020.106683) 11 | - Li, S., et al., An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network. Measurement, 2020. 165: p. 108122.[link](https://www.sciencedirect.com/science/article/pii/S0263224120306606?dgcid=rss_sd_all) 12 | - Chen, Z., et al., Domain Adversarial Transfer Network for Cross-domain Fault Diagnosis of Rotary Machinery. IEEE Transactions on Instrumentation and Measurement: p. 1-1.[link](https://ieeexplore.ieee.org/document/9099635) 13 | - Cheng, C., et al., Wasserstein distance based deep adversarial transfer learning for intelligent fault diagnosis with unlabeled or insufficient labeled data. Neurocomputing, 2020. 409: p. 35-45.[link](https://www.sciencedirect.com/science/article/pii/S0925231220308754?dgcid=rss_sd_all) 14 | - Zhang, Z., et al., Unsupervised domain adaptation via enhanced transfer joint matching for bearing fault diagnosis. Measurement, 2020: p. 108071.[link](https://www.sciencedirect.com/science/article/pii/S0263224120306096?dgcid=rss_sd_all) 15 | - Xu, X., et al., Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion. Measurement, 2020: p. 108086.[link](https://www.sciencedirect.com/science/article/pii/S0263224120306242?dgcid=rss_sd_all) 16 | - Haidong, S., et al., Intelligent fault diagnosis among different rotating machines using novel stacked transfer auto-encoder optimized by PSO. ISA Transactions, 2020.[link](https://doi.org/10.1016/j.isatra.2020.05.041) 17 | - Zou, L., Y. Li and F. Xu, An adversarial denoising convolutional neural network for fault diagnosis of rotating machinery under noisy environment and limited sample size case. Neurocomputing, 2020.[Link](https://doi.org/10.1016/j.neucom.2020.04.074) 18 | - Guo, S., et al., Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization. IEEE Transactions on Industrial Electronics, 2020. 67(9): p. 8005-8015.[link](https://ieeexplore.ieee.org/document/8848851) 19 | - Wang, H., et al., An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network. IEEE Transactions on Instrumentation and Measurement, 2020. 69(6): p. 2648-2657.[link](https://ieeexplore.ieee.org/document/8760507) 20 | - Mao, W., W. Feng and X. Liang, A novel deep output kernel learning method for bearing fault structural diagnosis. Mechanical Systems and Signal Processing, 2019. 117: p. 293-318.[link](https://www.sciencedirect.com/science/article/pii/S0888327018304357) 21 | - Zhang, W., et al., A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 2018. 100: p. 439-453.[link](https://doi.org/10.1016/j.ymssp.2017.06.022) 22 | - Zhao, D., T. Wang and F. Chu, Deep convolutional neural network based planet bearing fault classification. Computers in Industry, 2019. 107: p. 59-66.[link](https://doi.org/10.1016/j.compind.2019.02.001) 23 | - Jiao J, Zhao M, Lin J, et al. A comprehensive review on convolutional neural network in machine fault diagnosis[J]. arXiv preprint arXiv:2002.07605, 2020. [link](https://arxiv.org/ftp/arxiv/papers/2002/2002.07605.pdf) 24 | - Li, X., W. Zhang and Q. Ding, Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks. IEEE Transactions on Industrial Electronics, 2019. 66(7): p. 5525-5534. [link](https://ieeexplore.ieee.org/document/8456850) 25 | - Shao, S., P. Wang and R. Yan, Generative adversarial networks for data augmentation in machine fault diagnosis. Computers in Industry, 2019. 106: p. 85-93.[link](https://www.sciencedirect.com/science/article/pii/S0166361518305657?via%3Dihub) 26 | - Wen, L., et al., A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method. IEEE Transactions on Industrial Electronics, 2018. 65(7): p. 5990-5998.[link](https://ieeexplore.ieee.org/document/8114247) 27 | - Wen, L., L. Gao and X. Li, A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019. 49(1): p. 136-144.[link](https://ieeexplore.ieee.org/document/8058000) 28 | - Wen, L., X. Li and L. Gao, A New Two-Level Hierarchical Diagnosis Network Based on Convolutional Neural Network. IEEE Transactions on Instrumentation and Measurement, 2020. 69(2): p. 330-338.[link](https://ieeexplore.ieee.org/document/8649683) 29 | 30 | 31 | 32 | 33 | ## Gearbox fault diagnosis 34 | - Xing, S., et al., Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions. IEEE transactions on industrial electronics (1982), 2021. 68(3): p. 2617-2625.[link](https://ieeexplore.ieee.org/document/8998590) 35 | - Jiang, G., et al., Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox. IEEE Transactions on Industrial Electronics, 2019. 66(4): p. 3196-3207.[link](https://ieeexplore.ieee.org/document/8384293)---[Notes](../notes/papernotes1.md) 36 | 37 | - Hu, Z., et al., Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network. IEEE Transactions on Industrial Electronics, 2020. 67(4): p. 3216-3225. [link](https://ieeexplore.ieee.org/document/8704327/) 38 | 39 | 40 | 41 | 42 | 43 | 44 | ## Others 45 | - Chadha, G.S., et al., Bidirectional deep recurrent neural networks for process fault classification. ISA Transactions, 2020.[link](https://www.sciencedirect.com/science/article/pii/S0019057820302846) 46 | - Fu, S., et al., A re-optimized deep auto-encoder for gas turbine unsupervised anomaly detection. Engineering Applications of Artificial Intelligence, 2021. 101: p. 104199. 47 | [link](https://doi.org/10.1016/j.engappai.2021.104199) 48 | 49 | [:back:返回主目录](../README.md) 50 | -------------------------------------------------------------------------------- /doc/RULP.md: -------------------------------------------------------------------------------- 1 | # Remaining useful life prediction 2 | 3 | ## CNN 4 | - M. Zhao, S. Zhong, X. Fu, B. Tang, S. Dong and M. Pecht, "Deep Residual Networks With Adaptively Parametric Rectifier Linear Units for Fault Diagnosis," in IEEE Transactions on Industrial Electronics, vol. 68, no. 3, pp. 2587-2597, March 2021, doi: 10.1109/TIE.2020.2972458.[link](https://ieeexplore.ieee.org/document/8998530) 5 | - Wang, B., et al., Multi-Scale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery. IEEE transactions on industrial electronics (1982), 2020: p. 1-1.[link](https://ieeexplore.ieee.org/document/9126224) 6 | - Cheng, C., et al., A Deep Learning-Based Remaining Useful Life Prediction Approach for Bearings. IEEE/ASME Transactions on Mechatronics, 2020. 25(3): p. 1243-1254.[link](https://ieeexplore.ieee.org/document/8982045) 7 | - Wang, B., et al., Deep separable convolutional network for remaining useful life prediction of machinery. Mechanical Systems and Signal Processing, 2019. 134: p. 106330.[link](https://doi.org/10.1016/j.ymssp.2019.106330) 8 | - Wang, B., et al., Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery. Neurocomputing, 2019.[link](https://doi.org/10.1016/j.neucom.2019.10.064) 9 | - Yang, B., R. Liu and E. Zio, Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture. IEEE Transactions on Industrial Electronics, 2019. 66(12): p. 9521-9530.[link](https://ieeexplore.ieee.org/document/8752268) 10 | - Yu, W., I.Y. Kim and C. Mechefske, Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mechanical Systems and Signal Processing, 2019. 129: p. 764-780.[link](https://doi.org/10.1016/j.ymssp.2019.05.005) 11 | 12 | ## LSTM 13 | - Chen, Z., et al., Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach. IEEE transactions on industrial electronics (1982), 2021. 68(3): p. 2521-2531.[link](https://ieeexplore.ieee.org/document/8998569) 14 | - Yan, H., et al., Long-term gear life prediction based on ordered neurons LSTM neural networks. Measurement, 2020. 165: p. 108205.[link](https://doi.org/10.1016/j.measurement.2020.108205) 15 | - Ding, N., et al., Journal bearing seizure degradation assessment and remaining useful life prediction based on long short-term memory neural network. Measurement, 2020: p. 108215.[link](https://doi.org/10.1016/j.measurement.2020.108215) 16 | - Lyu, Y., et al., Joint Model for Residual Life Estimation Based on Long-Short Term Memory Network. Neurocomputing, 2020.[link](https://doi.org/10.1016/j.neucom.2020.06.052) 17 | - Xiang, S., et al., LSTM networks based on attention ordered neurons for gear remaining life prediction. ISA Transactions, 2020.[link](https://www.sciencedirect.com/science/article/pii/S001905782030269X?dgcid=rss_sd_all) 18 | - Sayah, M., et al., Robustness testing framework for RUL prediction Deep LSTM networks. ISA Transactions, 2020.[link](https://doi.org/10.1016/j.isatra.2020.07.003) 19 | 20 | 21 | 22 | 23 | [:back:](../README.md) 24 | -------------------------------------------------------------------------------- /doc/TD.md: -------------------------------------------------------------------------------- 1 | # Trend prediction 2 | 3 | [:back:返回主目录](../README.md) 4 | -------------------------------------------------------------------------------- /notes/papernotes1.md: -------------------------------------------------------------------------------- 1 | 2 | # TOC 3 | - [标题](#1标题) 4 | - [方法概要](#2方法概括) 5 | - [目标](#目标) 6 | - [背景理论与方法](#背景理论与方法) 7 | - [方法要点](#方法要点) 8 | - [核心创新点](#核心创新点) 9 | - [总结](#总结) 10 | - [关键文献](#3关键文献列表) 11 | - [好词好句](#4好词好句) 12 | - [补充笔记](#5笔记) 13 | 14 | --- 15 | 16 | # 笔记题目 17 | ## 1.标题 18 | Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox 19 | ### 发表日期 20 | 2019 21 | 22 | ### 作者 23 | Jiang, Guoqian 24 | He, Haibo 25 | Yan, Jun 26 | Xie, Ping 27 | ### 关键词 28 | fault diagnosis 29 | feature extraction 30 | gears 31 | learning (artificial intelligence) 32 | mechanical engineering computing 33 | neural nets 34 | pattern classification 35 | vibrational signal processing 36 | wind turbines 37 | signal processing 38 | CNN architecture 39 | intelligent fault diagnosis method 40 | health conditions 41 | raw vibration signals 42 | wind turbine gearbox 43 | multiscale convolutional neural networks 44 | WT gearbox test rig 45 | MSCNN approach 46 | hierarchical learning structure 47 | multiscale learning 48 | classification 49 | multiscale feature extraction 50 | multiscale convolutional neural network architecture 51 | end-to-end learning-based fault diagnosis system 52 | Feature extraction 53 | Fault diagnosis 54 | Vibrations 55 | Convolutional neural networks 56 | Wind turbines 57 | Machine learning 58 | Signal processing 59 | Convolutional neural network (CNN) 60 | classification 61 | deep learning 62 | intelligent fault diagnosis 63 | multiscale feature extraction 64 | wind turbine (WT) gearbox 65 | 66 | ### 发表期刊 67 | IEEE Trans. Ind. Electron. 68 | 69 | --- 70 | 71 | ## 2.方法概括 72 | ### 目标 73 | In this paper, our goal is to develop an end-to-end fault diagnosis system based on CNNs, which is motivated by its excellent feature learning ability. The desirable system can automatically learn and discover discriminative features from raw temporal vibration signals and then classify different health conditions of the WT gearbox. 74 | 75 | ### 背景理论与方法 76 | convolutional neural networks 77 | 78 | ### ==方法要点== 79 | 80 | ### ==核心创新点== 81 | The key idea of the proposed MSCNN is to incorporate multiscale feature learning ability into the traditional CNN architecture. 82 | 83 | 84 | ### 总结 85 | 86 | 87 | 88 | --- 89 | 90 | ## 3.关键文献列表 91 | - Li, C. and P. Shang, Multiscale Tsallis permutation entropy analysis for complex physiological time series. Physica A: Statistical Mechanics and its Applications, 2019. 523: p. 10-20.[link](https://www.sciencedirect.com/science/article/pii/S0378437119300317) 92 | - Cui, Z., W. Chen and Y. Chen, Multi-Scale Convolutional Neural Networks for Time Series Classification. 2016.[link](https://arxiv.org/abs/1603.06995) 93 | --- 94 | 95 | ## 4.好词好句 96 | 97 | ### 单词 98 | 99 | 100 | ### 句子 101 | 102 | --- 103 | 104 | 105 | ## 5.笔记 106 | 107 | 108 | - [返回顶部](#1标题) 109 | 110 | --------------------------------------------------------------------------------