研究生: |
莊于儀 Yu-I Chuang |
---|---|
論文名稱: |
整合隱藏馬可夫鏈及循序樣式探勘方法於筆電零件預防性維修之研究 Integrating Hidden Markov Chain and Sequential Pattern Mining in Preventive Maintenance of Gaming Laptops |
指導教授: |
歐陽超
Chao Ou-Yang |
口試委員: |
郭人介
Ren-Jieh Kuo 林希偉 Shi-Woei Lin |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 工業管理系 Department of Industrial Management |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 中文 |
論文頁數: | 75 |
中文關鍵詞: | 筆記型電腦 、隱藏馬可夫模型 、循序樣式探勘 |
外文關鍵詞: | laptops, Hidden Markov Model, sequential pattern mining |
相關次數: | 點閱:260 下載:0 |
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目前筆記型電腦維修流程中僅專注於排除客戶送修時所反饋之故障問題,並未針對筆電其他部分進行相關性檢查與維護保養措施,使得筆電存在著再次返修之可能性。又針對電競筆電使用者而言,電競筆電所使用之零件價格通常較為高昂,若有多次返修之情形發生,勢必須付出較多的維修成本,容易造成使用者產生厭煩感,進而影響售後服務品質並導致顧客滿意度下降。因此本研究希望透過隱藏馬可夫模型搭配循序樣式探勘探討筆記型電腦較常發生之更換零件序列。
本研究個案為國內知名電腦硬體製造商,並由該個案公司維修服務中心提供筆記型電腦維修資料。為了方便後續研究進行,將維修資料中筆電送修次數進行整理並將所換修之零件加以分類。首先運用隱藏馬可夫模型中Baum-Welch演算法及Viterbi演算法並找出不同故障問題序列所對應之更換零件序列。接著應用Yen's Algorithm找出多條更換零件序列。最後運用循序樣式探勘中Aprioriall演算法找出筆記型電腦較常發生之更換零件序列。
經由本研究所提出筆記型電腦維修與更換零件推估之參考資訊可作為服務人員於維修判斷時的輔助資訊,使得維修人員能事先知道後續可能損壞之零件,因而能進行初步檢查並確認相關零件的使用情形,以降低故障再次發生之機率、減少筆電再次返修之可能性、提升筆電售後服務品質並達到筆記型電腦維修與預防性保養之目的。
Currently, the laptop computer maintenance process only focuses on troubleshooting the existed problems, the inspection of related possible defect components are not carried. And hence make the laptop have the possibility of re-repairing and affect the service satisfaction. This research proposes an approach integrating hidden Markov chain and process mining methods to identifying the frequent pattern of defect components based on a gaming laptop maintenance dataset form a leading laptop manufacturer in Taiwan.
According to the data of failure problems and related defect components, the research applied Baum-Welch and Viterbi algorithms to construct the hidden Markov model of the failed components. Then, Yen’s algorithm was used to identify replacement parts for different sequences of fault problems based on the replaceable components data. Finally, a sequential pattern mining approach “Aprioriall” was used to find out the replacement parts sequence that occurs more frequently on gaming laptops.
The research results can be used to support the service people to identify the possible defect parts in advance and carried out possible preventive maintenance. The possibility of re-repair can be reduced and increase the service satisfaction.
朱丰瀛. (2017). 全球電競市場大戰 台PC廠火線競食. 新新聞. https://www.new7.com.tw/NewsView.aspx?t=04&i=TXT20171129164830EI0&fbclid=IwAR0ydsUukA3kkgByfuzcKuc5uNBTPBvI9EulPTAuiO1M-B_cnVD3pHuXye8
李俊緯. (2008). 售後服務對消費者選擇品牌影響之探討-以筆記型電腦產業為例. 國際企業學系. 臺北市, 玄奘大學. 碩士:97.
沈德麒. (2004). 資料探勘技術於航電元件維修備料預測之研究. 工業工程學系. 彰化縣, 大葉大學. 碩士:79.
阮玉華. (2009). 資料探勘應用於筆記型電腦電池組備料決策. 資訊管理學系. 新北市, 華梵大學. 碩士:58.
林峻煌. (2010). 基於服務品質管理觀點評量生產力維修即預防性維修於製造執行系統之效益探討. 資訊管理研究所. 高雄市, 國立中山大學. 碩士在職專班:76.
侯文堅. (2010). 售後服務品質、關係品質與顧客忠誠度關係之研究-以華碩筆記型電腦為例. 經營管理研究所. 新竹市, 國立交通大學. 碩士:75.
莊博文. (2002). 隱藏式馬可夫模型介紹. 國立台中師範學院教育測驗統計研究所.http://ntcuir.ntcu.edu.tw/bitstream/987654321/11557/1/48-2.pdf
陳延聖. (2006). 以手部關節3D座標為隱藏馬可夫模型輸入的動態手勢辨識. 工業管理系. 台北市, 國立臺灣科技大學. 碩士:73.
黃耀震. (2014). 利用HMM尋找疾病相關基因. 資訊工程系. 高雄市, 國立高雄應用科技大學. 碩士:58.
楊才旻. (2002). 瀏覽行為之HMM分析與參數估計. 資訊科學系. 臺北市, 東吳大學. 碩士:53.
維基百科. 預防性維護. https://zh.wikipedia.org/wiki/%E9%A0%90%E9%98%B2%E6%80%A7%E7%B6%AD%E4%BF%AE
顏嘉. (2005). 考量預防性維修下雙構面保顧模式之研究. 工業與資訊管理學系.台南市, 國立成功大學. 碩士:66.
Afzal, M.S., & AI-Dabbagh, A.W. (2017). Forecasting in industrial process control:A Hidden Markov Model approach. International Federation of Automatic Control, 50(1), 14770-14775.
Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Proceedings of the Eleventh International Conference on Data Engineering. IEEE Computer Society Press. 3-14.
Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17.
Fort, A., Mugnaini, M., & Vignoli, V. (2015). Hidden Markov Models approach used for life parameters estimations. Reliability Engineering and System Safety, 136, 85-91.
Gandhi, K., Schmidt, B., & Ng, A.H.C. (2018). Towards data mining based decision support in manufacturing maintenance. 51st CIRP Conference on Manufacturing Systems, 72, 261-265.
Kouemou, G. L. (2011). History and Theoretical Basics of Hidden Markov Models. Hidden Markov Models, Theory and Application, Przemyslaw Dymarski(Ed).
Han, J. Kamber, M., & Pei, J. (2001). Data mining concepts and techniques. Morgan kanfmann publish, 3rd ed.
Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. International Conference on Management of Data, ACM, 1-12.
Hand, D., Mannila, H., & Smyth, P. (2001). Principles of data mining. The MIT press, Cambridge, Massachusrees London England, 1st ed.
Lee, K. F., Hon, H. W., & Reddy, R., (1990). An overview of the SPHINX speech recognition system. IEEE Transaction on acoustics speech and signal processing, 38(1), 35-45.
Kumar Madan, K.M.V., & Srinivas, P. V. S. (2011). Algorithms for mining sequential patterns. International Journal of Information sciences and Application, 3, 59-69.
Lasfar, M., & Bouden, H. (2018). A method of data mining using Hidden Markov Models(HMMs) for protein secondary structure prediction. Procedia Computer Science, 127, 42-51.
Letourneau, S., Famili, F., & Matwin, S. (1999). Data mining for prediction of aircraft component replacement. IEEE Intelligent Systems and their Applications. 14(6), 59-66.
Mitchell, H., Marshall, A.H., & Zenga, M. (2015). Using the hidden Markov model to capture quality of care in Lombardy geriatric wards. Operation Research for Health Care, 7, 103-110.
Mooney, C. H., & Roddick, J. F. (2013). Sequential pattern mining – approaches and algorithms. ACM Computing Surveys, 45(2), 1-39.
Nilsson, M. (2005). First Order Hidden Markov Model: Theory and Implementation Issues. Sweden: Department of Signal Precessing, Blekinge Institute of Technology.
Nuanáin, C. Ó., Jordà, S., & Herrera, P. (2017). k-Best Hidden Markov Model Decoding for Unit Selection in Concatenative Sound Synthesis. International Symposium on Computer Music Multidisciplinary Research.
Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., et al. (2001). PrefixSpan mining sequential patterns efficiently by prefix projected pattern growth. International Conference of Data Engineering, 215-226.
Rabiner, L. R., & Juang, B. H. (1986). An introduction to Hidden Markov Model. IEEE ASSP Magazine, 3(1), 4-16.
Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257-286.
ReliaWiki. Introduction to Repairable Systems. http://reliawiki.org/index.php/Introduction_to_Repairable_Systems#Preventive_Maintenance_2
Seo, J. H., & Bai, D. S. (2004). An optimal maintenance policy for a system under periodic overhaul. Mathematical and Computer Modelling, 39(4-5), 373-380.
Slimani, T., & Lazzez, A. (2013). Sequential mining : patterns and algorithms analysis. International Journal of Computer and Electronics Research, 2(5), 639-647.
Srikant, R., & Agrawal, R. (1996). Mining sequential patterns : generalizations and performance improvements. In: EDBT’96 proceedings of the 5th International Conference on Extending Database Technology: advances in database technology, Berlin: Springer, 1057, 1-17.
Tan, P. N., Steinbach, M., & Kumar, V. (2007). Introduction to data mining. Addison-Wesley Longman Publishing Co., Inc., 1st ed.
Viger, P. F., Lin, W., Kiran, R. U., Koh, Y. S., & Thomas, R. (2017). A survey of sequential pattern mining. Data Science and Pattern Recognition, 1, 54-77.
Vijayarani, S., & Deepa, S. (2013). Sequential pattern mining - A study. International Conference on Research Trends in Computer Technologies(ICRTCT), 14-18.
Yeh, R. H., & Lo, H. C. (2001). Optimal preventive-maintenance warranty policy for repairable products. European Journal of Operational Research, 134, 59-69.
Zaki, M. J. (2001). SPADE: An efficient algorithm for mining frequent sequence. Machine Learning, 42(1), 31-60.