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姓名	麥柔惠(Jou-Hui,Mai)  查詢紙本館藏	畢業系所	資訊管理學系在職專班
論文名稱	深度學習於車輛零件及保養時間預測之應用研究 (Research on the Application of Deep Learning in Vehicle Parts and Maintenance Time Prediction)
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檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2029-7-1以後開放)
摘要(中)	車輛產業本身為較封閉的產業而商用車更為封閉且少為人知的產業，經銷商會遇到零件庫存的問題導致庫存成本及客戶等待抱怨等問題，這些問題可以延伸到庫存管理、零件成本、客戶營業損失賠償等。 在過往歷史上受限於車輛產業的資料封閉大多研究停留在車廠內部，導致相關研究斷斷續續，研究無法延伸也無法使用舊有資料重現比較，經銷商多為傳統產業較少有應用機器學習優化服務流程的能力，現有相關研究大多注重在模型而不是實際應用，本研究使用時下的機器學習技術搭配某S公司實際車輛維修履歷，預測客戶”下次”回廠的時間及”下次”維修的零件。 本研究使用注意力機制Transformer輸入車輛的車齡、里程數、車型、保固情況，預測零件更換(多類別標籤的分類任務)及回廠時間(迴歸任務)，並比較了其他時間序列的模型效能，實驗各特徵對準確率的重要性。 本研究成果展示了深度學習技術在車輛售後服務領域應用的可行性，期望提高汽車行業的效率、降低成本並提升客戶滿意度。
摘要(英)	The vehicle industry is relatively closed, and the commercial vehicle industry is even more closed and less well-known. Dealers often face problems with parts inventory, leading to inventory costs and customer waiting complaints. These issues can extend to inventory management, parts costs, and compensation for customer business losses. In the past, due to the closed nature of data in the vehicle industry, most research was limited to internal studies within vehicle manufacturers, resulting in intermittent research that could not be extended or replicated using existing data. Dealers, often traditional industries, have limited ability to apply machine learning to optimize service processes. Existing related research mostly focuses on models rather than practical applications. This study uses current machine learning techniques combined with actual vehicle maintenance records from Company S to predict the time of a customer′s "next" visit to the service center and the parts to be repaired during the "next" visit. This study employs the attention mechanism Transformer, inputting vehicle age, mileage, model, and warranty status to predict parts replacement (a multi-class classification task) and the time of the next service visit (a regression task). The performance of other time series models is compared, and the importance of each feature for accuracy is experimented. The results of this study demonstrate the feasibility of applying deep learning techniques in the field of vehicle after-sales service, with the expectation of improving efficiency, reducing costs, and enhancing customer satisfaction in the automotive industry.
關鍵字(中)	★ 深度學習 ★ 車輛零件 ★ 維護時間預測	關鍵字(英)	★ deep learning ★ vehicle parts ★ maintenance time prediction ★ RUL ★ BERT ★ predictive maintenance
論文目次	摘要 I ABSTRACT II 目錄 III 表目錄 V 圖目錄 VI 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機 4 1.3 研究目的 5 第二章 文獻探討 6 2.1 保養預測相關文獻 6 2.2 預測技術相關文獻 9 第三章 研究方法 13 3.1 資料來源和前處理 14 3.2 模型的架構 20 3.3 損失函數 24 第四章 實驗結果與分析 26 4.1 模型比較對象 27 4.2 模型效能與評估 28 4.3 ∆t的重要性討論 32 4.4 特徵的重要性討論 33 4.5 損失函數比較 36 4.6 敏感性分析與參數設定與訓練策略 38 4.7 個案分析 47 第五章 結論與未來研究 56 5.1 結論 56 5.2 未來研究方向 56 參考文獻 58
參考文獻	[1] 賴文祥(wen-hsiang lai), & 李涵恕(han-su lee)，以「沉浸理論」與「排隊等待結構」探討顧客之「等待時間知覺」，東亞論壇，2013。479:65-84 [2] S. Tayala, S.R.Singhb and R. Sharma, " An inventory model for deteriorating items with seasonal products and an option of an alternative market ",Uncertain Supply Chain Managemnent,3,pp.69-86,2014 [3] N. Zhang, W. Si,” Deep Reinforcement Learning for Condition-Based Maintenance Planning of Multi-Component Systems Under Dependent Competing Risks”, Reliability Engineering and System Safety,2020 [4] K. Patrick, D. Bo, K. Iluju, Y. Tet,” IoT-Based predictive maintenance for fleet management”,Procedia Computer Science, vol. 151,pp.607-613,2019 [5] J. Gardner , J. Mroueh , N. Jenuwine , N. Waverdyck , S. Krassenstein , A. Farahi , D. Koutra,” Driving with data in the motor city: Mining and modeling vehicle fleet maintenance data”, University of Michigan,2020 [6] R. Khoshkangini, P. S. Mashhadi, P. Berck, S. G. Shahbandi, S. Pashami, S. Nowaczyk, T. Niklasson,” Early prediction of quality issues in automotive modern industry”, Reliability Engineering & System Safety, vol. 215, no. 19,2021 [7] C. Chen, Y. Liu,” Automobile maintenance modelling using gcforest.”, In Proceedings of the 16th IEEE Conference on Automation Science and Engineering on Automation Science and Engineering (CASE),2020 [8] 盧宣文, “發展核密度動態集成技術於預測保養”, 國立成功大學工程管理碩士在職專班,2020 [9] M. Rezvani, M. AbuAli, S. Lee, J. Lee, J. Ni, ” A comparative analysis of techniques for electric vehicle battery prognostics and health management (PHM)”, In Proceedings of the Society of Automobile Engineer Technical Paper on Commercial Vehicle Engineering Congress,pp.600-605,2011 [10] Z.-H. Wang, Hendrick, G.-J. Horng, , H.-T. Wu, G.-J. Jong,” A prediction method for voltage and lifetime of lead–acid battery by using machine learning”,Enegy Exploration Exploitation Vol. 38, no. 1, pp310-329,2020 [11] R. Prytz,S. Nowaczyk, T. Rögnvaldsson, S. Byttner,” Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data”,Engineering Applications of Artificial Intelligence, Vol. 41, pp.139-150, 2015 [12] M. Taie, M. Diab, M. Elhelw,” Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms” , In Proceedings of the Society of Automobile Engineer International Journal of Passenger Cars - Electronic and Electrical Systems, vol. 9, no. 1,pp.114-122, 2016 [13] C.-Y. Lee, T.-S. Huang, M.-K. Liu, C.-Y. Lan,” Data science for vibration heteroscedasticity and predictive maintenance of rotary bearings”,MDPI Energies, 2019 [14] S. Al-Dahidi, F. D. Maio, P. Baraldi, E. Zio, ” Remaining useful life estimation in heterogeneous fleets working under variable operating conditions”, Reliability Engineering & System Safety, Vol. 156, pp.109-124, 2016 [15] G.P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model”, Neurocomputing Vol 50, p.159-175,2003 [16] Z. C. Lipto, J. Berkowitz,” A Critical Review of Recurrent Neural Networks for Sequence Learning”, University of California, San Diego ,2015 [17] S. Hochreiter, J. Schmidhuber, “Long Short-term Memory”, Neural Computation, vol. 9, no. 8, p.1735-1880,1997 [18] F. Gers, J. Schmidhuber, F. Cummins, “Learning to Forget: Continual Prediction with LSTM”,Technical Report, vol. 12, no.10, p.2451-2471, 2000 [19] M. Schuster, K. K. Paliwal, "Bidirectional recurrent neural networks," in IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673-2681,1997 [20] Y. Wu, Q. Xue, J. Shen, Z. Lei, Z. Chen, Y. Liu,”State of health estimation for lithium-ion batteries based on healthy features and long short-term memory.”, Institute of Electrical and Electronics Engineers (IEEE), vol. 8, pp.28533–28547, 2020 [21] G. You, S. Park, D. Oh,” Diagnosis of electric vehicle batteries using recurrent neural networks”, Institute of Electrical and Electronics Engineers (IEEE) Trans Ind Electron, vol. 64, no.6, pp.4885–4893, 2017 [22] P. Wolf, A. Mrowca, TT. Nguyen, B. Baker, S. Gunnemann,“Pre-ignition detection using deep neural networks: A step towards data-driven automotive diagnostics.”,In proceedings: IEEE conference on intelligent transportation systems(ITSC),vol. 2018, p.176–183,2018 [23] K. Cho, D. Bahdanau, B. Merrienboer, C. Gulcehre, F. Bougares, H. Schwenk, Y. Bengio, “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation”, In proceedings: Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 1724–1734,2014 [24] D. Rengasamy, M. Jafari, B. Rothwell, X. Chen, “Deep learning with dynamically weighted loss function for sensor-based prognostics and health management”. Sensors, Vo. 20, no. 3,2020 [25] J. Zuo, H. Lv, D. Zhou, Q. Xue, L. Jin, W. Zhou, “Deep learning based prognostic framework towards proton exchange membrane fuel cell for automotive application”, Applied Energy, vol. 281,pp.115937,2021 [26] Z.C, Lipton, D. C. Kale, C.P. Elkan, R.C. Wetzel, “Learning to Diagnose with LSTM Recurrent Neural Networks” , in Proceedings of the International Conference on Learning Representations, 2016 [27] A. Vaswani, N.M. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, “Attention is all you need”, In proceeding: 31th Conference on Neural Information Processing Systems (NIPS), 2017 [28] F. Karim, M. Somshubra, D. Houshang, H. Samuel, ”Multivariate LSTM-FCNs for time series classification”, Neural Networks, vol. 116,pp.237-245,2019 [29] W. Kang and J. McAuley, “Self-Attentive Sequential Recommendation,” in Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM’18), pp. 197–206, 2018. [30] D. Huynh and E. Elhamifar, "A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning," 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8773-8783, 2020 [31] R. You, Z. Zhang, Z. Wang, S. Dai, H. Mamitsuka, S. Zhu, “ AttentionXML: label tree-based attention-aware deep model for high-performance extreme multi-label text classification”, in Proceedings of the 33rd International Conference on Neural Information Processing Systems. No.523, pp.5820–5830,2019 [32] D. Papatheodoulou, P. Pavlou, S.G. Vrachimis, K. Malialis, D.G. Eliades, T. Theocharides, “A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring”, in Proceedings of Artificial Intelligence Applications and Innovations(AIAI 2022) IFIP Advances in Information and Communication Technology, vol 647. Springer,2022 [33] A. Theissler, J. Pérez-Velázquez, M. Kettelgerdes, G. Elger, ” Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry”, Reliability Engineering and System Safety, vol. 215, 2021 [34] J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, “Pre-training of deep bidirectional transformers for language understanding.”, In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol 1, pp. 4171–4186,2019 [35] Z. Dai, Z. Yang, Y. Yangl, J. Carbonell, Q. V. Le, R. Salakhutdinov ,”Transformer-xl: Attentive language models beyond a fixed-length context.” In Proceedings of Annual Meeting of the Association for Computational Linguistics [36] K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition”. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp. 770-778,2016 [37] D. Hendrycks, K. Gimpel, ” Gaussian error linear units (gelus)”, University of California, Berkeley & Toyota Technological Institute at Chicago,2016 [38] Y. Huang, J. Qi, X. Wang and Z. Lin, "Asymmetric Polynomial Loss for Multi-Label Classification," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, pp. 1-5,2023 [39] Y. Zhang, Y. Cheng, X. Huang, F. Wen, R. Feng, Y. Li, Y. Guo, “Simple and Robust Loss Design for Multi-Label Learning with Missing Labels”. ArXiv, abs/2112.07368,2021 [40] T. -Y. Lin, P. Goyal, R. Girshick, K. He and P. Dollár, "Focal Loss for Dense Object Detection," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 318-327,2020 [41] D. P. Kingma, J. Ba, “ Adam: A method for stochastic optimization”, International Conference on Learning Representations of Computer Science, Mathematics,2014 [42] P. Liashchynskyi , P. Liashchynskyi, “Grid search, random search, genetic algorithm: A big comparison for NAS”, Department of Computer Engineering Ternopil National Economic University, arXiv preprint arXiv:1912.06059,2019 [43] A. Tharwat, “Classification assessment methods”, Applied Computing and Informatics, vol. 17, No. 1, pp. 168-192, 2021 [44] A. R. Sahu, S. K. Pslei, A. Mishra, “Data-driven fault diagnosis approaches for industrial equipment: A review”, Expert System Special Issue:Machine Learning Challenges and Applications for Industry 4.0 (EXSYS‐MLI4.0),vol. 41, Issue 2,2023 [45] Z. Chen, M. Wu, R. Zhao, F. Guretno, R. Yan, X. li, “Machine Remaining Useful Life Prediction via an Attention Based Deep Learning Approach” IEEE Transactions on Industrial Electronics, vol. 68, Issue. 3, 2021 [46] K. Janocha, & W. M. Czarnecki, "On Loss Functions for Deep Neural Networks in Classification." , Schedae Informaticae, vol. 25, pp. 49 - 59,2017 [47] L. Yi, L. Zhang, X. Xu and J. Guo, "Multi-Label Softmax Networks for Pulmonary Nodule Classification Using Unbalanced and Dependent Categories," in IEEE Transactions on Medical Imaging, vol. 42, no. 1, pp. 317-328, 2023 [48] V. Dang, M. Bendersky, W.B. Croft, ” Two-Stage Learning to Rank for Information Retrieval”, In proceeding of European Conference on Information Retrieval,2013
指導教授	陳以錚(Yi-Cheng,Chen,)	審核日期	2024-7-11
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