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研究生:陳柏翔
研究生(外文):CHEN, BO-XIANG
論文名稱:利用生成對抗網路產生模擬資料以提昇刀具磨耗預測之準確率
論文名稱(外文):Improve the Accuracy of Tool Wearing Prediction by Using the Mock Data Generated by Generative Adversarial Networks
指導教授:蘇純繒蘇純繒引用關係
指導教授(外文):SU, CHWEN-TZENG
口試委員:陳奕中鍾毓驥李孟樺陳思翰
口試委員(外文):CHEN, YI-CHUNGCHUNG, YU-CHILI, MENG-HUACHEN, SSU-HAN
口試日期:2021-06-07
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:58
中文關鍵詞:刀具磨耗預測生成對抗網路深度學習模型集成式學習
外文關鍵詞:Tool WearGenerative Adversarial NetworkDeep LearningEnsemble Learning
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刀具壽命一直是工廠所重視的問題之一。一個好的刀具監控系統,可以讓工廠準確判斷刀具更換的時間點,以減少產品損壞。一般來說,建立刀具監控系統需要大量的刀具磨耗數據,讓系統能夠更加完善,但在蒐集數據上會造成耗費太多時間與人力。另外,現有的大多數研究也僅考慮了單一模型進行刀具磨耗分類,導致能提升的績效有限。因此,本研究提出了GCEL架構(GAN-CNN-Ensemble Learning)來解決過往研究的問題。首先,利用生成對抗網路產生模擬實際狀況資料,降低工廠蒐集數據的成本,接著,使用卷積類神經網路進行分類,並結合集成式學習框架提升模型分類準確性,最後,本研究使用原始刀具磨耗資料進行驗證,結果證明本研究所提出的架構能夠有效提升刀具磨耗分類的準確率。
Tool life is one of the many important issues in any factory. A good tool monitoring system allows a factory to accurately determine the tool replacement time and reduce damage to products. Establishing a tool monitoring system generally requires a large amount of data on tool wear to make the system more complete; however, collecting data often requires too much time and too many resources. Most studies have only considered a single model to classify tool wear, resulting in limited improvements in performance. In this paper, we propose the use of GCEL Architecture (GAN-CNN-Ensemble Learning) to solve the aforementioned problems by (1) using the Generative Adversarial Network to simulate actual situation data to reduce the cost of data collection and (2) using a convolutional neural network combined with ensemble learning to improve the accuracy of classification in this model. Original tool wear data were used for verification. The results show that the structure proposed in this paper can effectively improve the accuracy of tool wear classification.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究限制 3
1.4 論文架構 3
第二章 文獻回顧 4
2.1 刀具磨耗預測方法 4
2.1.1 影響刀具磨耗因素 4
2.1.2 機器學習 5
2.1.3 深度學習 7
2.2 數據生成 8
2.2.1 上採樣(Oversampling) 9
2.2.2 下採樣(Downsampling) 12
2.3 集成式學習(Ensemble Learning) 13
2.4 小結 15
第三章 資料集 16
3.1 資料集介紹 16
3.2 資料集觀察 16
3.3 處理資料集所遇到的困難 19
第四章 研究方法 20
4.1 資料清洗 20
4.1.1 線性補值 20
4.1.2 時序矩陣資料 21
4.1.3 利用生成對抗網路增生資料 21
4.2 利用時域與頻域提取重要特徵值 24
4.2.1 時序特徵提取 25
4.2.2 快速傅立葉轉換 26
4.2.3 連續小波轉換 26
4.3 卷積類神經網路 27
4.4 結合集成式學習提昇刀具磨耗分類準確性 30
第五章 實驗模擬 32
5.1 資料前處理 32
5.2 平衡資料集與結果 32
5.2.1 資料平衡結果 32
5.2.2 混淆矩陣結果 34
5.2.3 敏感度分析(ROC曲線與mAP曲線) 37
5.2.4 小結 42
5.3 提升績效方法 42
第六章 結論與未來研究 43
參考文獻 44
[1]A. Bustillo, and J. J. Rodríguez, "Online breakage detection of multitooth tools using classifier ensembles for imbalanced data," International Journal of Systems Science, vol. 45, no. 12, pp. 2590-2602, 2014.
[2]A. Duerden, F.E. Marshall, N. Moon, C. Swanson, K. Donnell and G.S. Grubbs II , "A chirped pulse Fourier transform microwave spectrometer with multi-antenna detection," Journal of Molecular Spectroscopy, vol. 376, pp. 111396, 2021.
[3]A. Ishaq, S. Sadiq, M. Umer, S. Ullah, S. Mirjalili, V. Rupapara, and M. Nappi, "Improving the prediction of heart failure patients' survival using SMOTE and effective data mining techniques," IEEE Access, vol. 9, pp. 39707-39716, 2021.
[4]A. P. Bradley, "The use of the area under the ROC curve in the evaluation of machine learning algorithms," Pattern recognition, vol. 30, no. 7, pp. 1145-1159, 1997.
[5]A. R. Hassan, and M. I. H. Bhuiyan, "Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting," Computer methods and programs in biomedicine, vol. 140, pp. 201-210, 2017.
[6]A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, "Learning from simulated and unsupervised images through adversarial training," Proceeding on IEEE Conf. on computer vision and pattern recognition, pp. 2107-2116, 2017.
[7]B. Krawczyk, M. Galar, Ł. Jeleń, and F. Herrera, "Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy," Applied Soft Computing, vol. 38, pp. 714-726, 2016.
[8]C. C. Hsu, C. Y. Lee, and Y. X. Zhuang, "Learning to detect fake face images in the wild," Proceeding on International Symposium on Computer, Consumer and Control, pp. 388-391, 2018.
[9]C. Cheng, J. Li, Y. Liu, M. Nie, and W. Wang, "Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding," Computers in Industry, vol. 106, pp. 1-13, 2019.
[10]C. Shi, G. Panoutsos, B. Luo, H. Liu, B. Li, and X. Lin, "Using multiple-feature-spaces-based deep learning for tool condition monitoring in ultraprecision manufacturing," IEEE Transactions on Industrial Electronics, vol. 66, Issue. 5, pp. 3794-3803, 2018.
[11]C. Shorten, and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of Big Data, vol. 6, no. 1, pp. 1-48, 2019.
[12]C. Sun, P. Wang, R. Yan, R. X. Gao, and X. Chen, "Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization," Mechanical Systems and Signal Processing, vol. 114, pp. 25-34, 2019.
[13]C. Zhang, X. Yao, J. Zhang, andE. Liu, "Tool wear monitoring based on deep learning," Comput. Integr. Manuf. Syst, vol. 23, on. 10, pp. 2146-2155, 2017.
[14]D. F. Hesser, and B. Markert, "Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks," Manufacturing letters, vol. 19, pp. 1-4, 2019.
[15]D. H. Wolpert, "Stacked generalization," Neural networks, vol. 5, no. 2, pp. 241-259, 1992.
[16]D. Kong, Y. Chen, and N. Li, "Gaussian process regression for tool wear prediction," Mechanical systems and signal processing, vol. 104, pp. 556-574, 2018.
[17]D. Xue, X. Zhou, C. Li, Y. Yao, M. Rahaman, J. Zhang, H. Chen, J. Zhang, S. Qi, and H. Sun, "An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification," IEEE Access, vol. 8, pp. 104603-104618, 2020.
[18]F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data," Mechanical Systems and Signal Processing, vol. 72, pp. 303-315, 2016.
[19]International Organization for Standardization (Ginebra), ISO 3685: Tool-life Testing with Single-point Turning Tools. ISO, 1993.
[20]J. Deng, G. Pang, Z. Zhang, Z. Pang, H. Yang, and G. Yang, "cGAN based facial expression recognition for human-robot interaction," IEEE Access, vol. 7, pp. 9848-9859, 2019.
[21]J. Mathew, C. K. Pang, M. Luo, and W. H. Leong, "Classification of imbalanced data by oversampling in kernel space of support vector machines," IEEE transactions on neural networks and learning systems, vol. 29, no. 9, pp. 4065-4076, 2017.
[22]J. Shijie, W. Ping, J. Peiyi, and H. Siping, "Research on data augmentation for image classification based on convolution neural networks," Proceeding on IEEE Conf. on Chinese automation congress, pp. 4165-4170, 2017.
[23]J. Viola, Y. Q. Chen, and J. Wang, "FaultFace: Deep convolutional generative adversarial network (DCGAN) based ball-bearing failure detection method," Information Sciences, vol. 542, pp. 195-211, 2021.
[24]J. Wang, J. Xie, R. Zhao, L. Zhang, and L. Duan, "Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing," Robotics and computer-integrated manufacturing, vol. 45, pp. 47-58. 2017.
[25]J. Wei, Z. Lu, K. Qiu, P. Li, and H. Sun, "Predicting Drug Risk Level from Adverse Drug Reactions Using SMOTE and Machine Learning Approaches," IEEE Access, vol. 8, pp. 185761-185775, 2020.
[26]K. Koga, "Signal processing approach to mesh refinement in simulations of axisymmetric droplet dynamics," Journal of Computational and Applied Mathematics, vol. 383, pp. 113131, 2021.
[27]L. Breiman, "Bagging predictors," Machine learning, vol. 24, no. 2, pp. 123-140, 1996.
[28]L. Perez, and J. Wang, "The effectiveness of data augmentation in image classification using deep learning," arXiv preprint, arXiv:1712.04621, 2017.
[29]M. C. Gomes, L. C. Brito, M. B. da Silva, and M. A. V. Duarte, "Tool wear monitoring in micromilling using Support Vector Machine with vibration and sound sensors," Precision Engineering, vol. 67, pp. 137-151, 2021.
[30]M. Jalayer, C. Orsenigo, and C. Vercellis, "Fault detection and diagnosis for rotating machinery: A model based on convolutional LSTM, Fast Fourier and continuous wavelet transforms," Computers in Industry, vol. 125, pp. 103378, 2021.
[31]M. Koziarski, B. Kwolek, and B. Cyganek, "Convolutional neural network-based classification of histopathological images affected by data imbalance," Proceeding on Video Analytics. Face and Facial Expression Recognition, pp. 1-11, 2018.
[32]M. S. H. Bhuiyan, I. A. Choudhury, and Y. Nukman, "An innovative approach to monitor the chip formation effect on tool state using acoustic emission in turning," International Journal of Machine Tools and Manufacture, vol. 58, pp. 19-28, 2012.
[33]M. S. H. Bhuiyan, I. A. Choudhury, M. Dahari, and Y. Nukman, "Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring," Measurement, vol. 92, pp. 208-217, 2016.
[34]M. Wang, J. Zhou, J. Gao, Z. Li, and E. Li, "Milling Tool Wear Prediction Method Based on Deep Learning Under Variable Working Conditions," IEEE Access, vol. 8, pp. 140726-140735, 2020.
[35]N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, vol. 16, pp. 321-357, 2002.
[36]P. Kumari, and D. Toshniwal, "Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance," Journal of Cleaner Production, vol. 279, pp. 123285, 2021.
[37]Q. Sun, and Z. Ge, "Deep learning for industrial KPI prediction: When ensemble learning meets semi-supervised data," IEEE Transactions on Industrial Informatics, vol. 17, no. 1, pp. 260-269, 2020.
[38]Q. Wu, P. Wang, C. Shen, I. Reid, and A. Van Den Hengel "Are you talking to me? reasoned visual dialog generation through adversarial learning," Proceeding on IEEE Conf. on Computer Vision and Pattern Recognition, pp. 6106-6115, 2018.
[39]R. Corne, C. Nath, M. El. Mansori, and T. Kurfess, "Study of spindle power data with neural network for predicting real-time tool wear/breakage during inconel drilling," Journal of Manufacturing Systems, vol. 43, pp. 287-295, 2017.
[40]R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen, and J. Wang, "Machine health monitoring using local feature-based gated recurrent unit networks," IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1539-1548, 2017.
[41]R. Zhao, R. Yan, J. Wang, and K. Mao, "Learning to monitor machine health with convolutional bi-directional LSTM networks," Sensors, vol. 17, no. 2, pp. 273, 2017.
[42]S. Dolinšek, and J. Kopač, "Acoustic emission signals for tool wear identification," Wear, vol. 225, pp. 295-303, 1999.
[43]Ş. Ertürk, and O. Kayabaşi, "Investigation of the Cutting Performance of Cutting Tools Coated With the Thermo-Reactive Diffusion (TRD) Technique," IEEE Access, vol. 7, pp. 106824-106838, 2019.
[44]S. F. Abdoh, M. A. Rizka, and F. A. Maghraby, "Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques," IEEE Access, vol. 6, pp. 59475-59485, 2018.
[45]S. K. Jalali, H. Ghandi, and M. Motamedi, "Intelligent condition monitoring of ball bearings faults by combination of genetic algorithm and support vector machines," Journal of Nondestructive Evaluation, vol. 39, no. 1, pp. 1-12, 2020.
[46]T. Miyato, and M. Koyama, "cGANs with projection discriminator," arXiv preprint, arXiv:1802.05637, 2018.
[47]T. Mohanraj, J. Yerchuru, H. Krishnan, R. S. Nithin Aravind, and R. Yameni, "Development of tool condition monitoring system in end milling process using wavelet features and Hoelder's exponent with machine learning algorithms," Measurement, vol. 173, pp. 108671, 2021.
[48]T. Pinto, I. Praça, Z. Vale, and J. Silva, "Ensemble learning for electricity consumption forecasting in office buildings," Neurocomputing, vol. 423, pp. 747-755, 2021.
[49]W Xu, K.J. Xu, X.L. Yu, Y. Huang, and W.K. Wu, "Signal processing method of bubble detection in sodium flow based on inverse Fourier transform to calculate energy ratio," Nuclear Engineering and Technology, 2021.
[50]W. Jiang, Y. Hong, B. Zhou, X. He, and C. Cheng, "A GAN-based anomaly detection approach for imbalanced industrial time series," IEEE Access, vol. 7, pp. 143608-143619, 2019.
[51]W. R. Tan, C. S. Chan, H. E. Aguirre, and K. Tanaka"ArtGAN: Artwork synthesis with conditional categorical GANs," Proceeding on IEEE Conf. on Image Processing, pp. 3760-3764, 2017.
[52]X. Yang, Y. Xu, Y. Quan, and H. Ji, "Image denoising via sequential ensemble learning," IEEE Transactions on Image Processing, vol. 29, pp. 5038-5049, 2020.
[53]Y. Freund, and R. E. Schapire, "Experiments with a new boosting algorithm," In icml, vol. 96, pp. 148-156, 1996.
[54]Y. Shi, D. Deb, and A.K. Jain, "Warpgan: Automatic caricature generation," Proceeding on IEEE Conf. on Computer Vision and Pattern Recognition, pp. 10762-10771, 2019.
[55]Z. Li, R. Liu, and D. Wu., "Data-driven smart manufacturing: tool wear monitoring with audio signals and machine learning," Journal of Manufacturing Processes, vol. 48, pp. 66-76, 2019.
[56]賴屹民(譯)(民109)。精通機器學習:使用 Scikit-Learn,keras與TensorFlow(原作者:Aurélien Géron)。台北市:碁峰資訊。(原著出版年:2019)
[57]2010 phm society conference data challenge: https://www.phmsociety.org/competition/phm/10.

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