紡織業捲取機之故障原因關聯分析

Association Analysis on the Causes of Winder Failure in Textile Industry

10.6220/joq.202204_29(2).0003

林欣儀(Hsin-yi Lin)；林真如(Chen-ju Lin)

預防保養 ； 關聯規則 ； 先驗演算法 ； preventive maintenance ； association rule ； Apriori algorithm

品質學報

29卷2期（2022 / 04 / 30）

136 - 150

繁體中文

紡絲製程經由原料熔融、抽絲、延伸，再將纖維束導入捲取機（winder），透過高速轉動將原絲捲繞成絲餅（bobbin）成品。絲餅捲繞為重要製程步驟，利用高速紡絲可使紗線均勻與結構蓬鬆，若捲取機發生機臺速度異常或其他故障，將會導致斷絲或切絲不順等問題，使得絲餅產生瑕疵。本研究以臺灣某化學纖維廠為例，應用統計分析與資料探勘技術，分析公司捲取機的故障紀錄，建立捲取機維修準則。研究方法利用關聯規則（association rule）學習領域中的先驗演算法（Apriori algorithm），發掘捲取機故障原因之間的關聯性。在分析結果所獲得的24條故障原因關聯規則中，12條與廠內維修人員既有經驗相符，另外12條則提供維修人員新的故障原因關聯資訊，以供維修人員參考。採用本研究成果所建立的捲取機故障原因關聯規則，可協助案例公司維修人員在捲取機發生故障時，無論其維修經驗多寡，均能快速判斷機臺失效相關原因，降低維修時間，提高維修效率。

In the spinning process, the raw material is firstly melted, drawn, and stretched to become the fiber bundle. The fiber bundle is then introduced into the winder by high-speed rotation to be wound into the final product, bobbin. Bobbin winding is an important process. Using high-speed spinning can make the yarn uniform and fluffy. Abnormal rotation speed or other winder failures will cause broken yarn, irregular cutting or other problmes that will lead to defective bobbins. This research studies the maintenance problem of a chemical fiber factory in Taiwan. The statistical analysis and the data mining techniques are used to analyze the winder failure records of the factory, and to establish the guidelines for winder maintenance. The research method utilizes the Apriori algorithm in the domain of Association rule learning to discover the association among the causes of winder failure. Among the 24 association rules obtained from the analysis, 12 rules are consistent with the existing experience of the maintenance staffs in the factory. The other 12 rules provide the maintenance staffs with new information of the association among the causes of winder failure for the staffs to refer to. Adopting the association rules established by this research can assist the maintenance staffs in quickly determining the relevant causes of winder failure, regardless of maintenance experience of the staffs. Thus, the maintenance time can be reduced and the maintenance efficiency can be improved.

1. Accorsi, R.,Manzini, R.,Pascarella, P.,Patella, M.,Sassi, S.(2017).Data mining and machine learning for condition-based maintenance.Procedia Manufacturing,11,1153-1161.
2. Agrawal, R.,Imieliński, T.,Swami, A.(1993).Mining association rules between sets of items in large databases.SIGMOD Record,22(2),207-216.
3. Agrawal, R.,Srikant, R.(1994).Fast algorithms for mining association rules in large databases.Proceedings of the 20th International Conference on Very Large Data Bases, Morgan Kaufmann,San Francisco, CA:
4. Anderson, T. W.,Darling, D. A.(1954).A test of goodness of fit.Journal of the American Statistical Association,49(268),765-769.
5. Bruzzese, D.,Davino, C.(2008).Visual mining of association rules.Visual Data Mining: Theory, Techniques and Tools for Visual Analytics,Berlin, Germany:
6. Carvalho, T. P.,Soares, F. A. A. M. N.,Vita, R.,Francisco, R. D. P.,Basto, J. P.,Alcalá, S. G. S.(2019).A systematic literature review of machine learning methods applied to predictive maintenance.Computers & Industrial Engineering,137,106024.
7. de Jonge, B.,Scarf, P. A.(2020).A review on maintenance optimization.European Journal of Operational Research,285(3),805-824.
8. Ding, S.-H.,Kamaruddin, S.(2015).Maintenance policy optimization—literature review and directions.The International Journal of Advanced Manufacturing Technology,76(5–8),1263-1283.
9. Djatna, T.,Alitu, I. M.(2015).An application of association rule mining in total productive maintenance strategy: an analysis and modelling in wooden door manufacturing industry.Procedia Manufacturing,4,336-343.
10. Grabot, B.(2020).Rule mining in maintenance: analysing large knowledge bases.Computers & Industrial Engineering,139,105501.
11. Ilangkumaran, M.,Kumanan, S.(2009).Selection of maintenance policy for textile industry using hybrid multi-criteria decision making approach.Journal of Manufacturing Technology Management,20(7),1009-1022.
12. Kammoun, M. A.,Rezg, N.(2018).Toward the optimal selective maintenance for multi-component systems using observed failure: applied to the FMS study case.The International Journal of Advanced Manufacturing Technology,96(1–4),1093-1107.
13. Kirubakaran, B.,Ilangkumaran, M.(2016).Selection of optimum maintenance strategy based on FAHP integrated with GRA–TOPSIS.Annals of Operations Research,245(1–2),285-313.
14. Momeni, M.,Fathi, M. R.,Zarchi, M. K.,Azizollahi, S.(2011).A fuzzy TOPSIS-based approach to maintenance strategy selection: a case study.Middle-East Journal of Scientific Research,8(3),699-706.
15. Mosaddar, D.,Shojaie, A. A.(2013).A data mining model to identify inefficient maintenance activities.International Journal of System Assurance Engineering and Management,4(2),182-192.
16. Özcan, E. C.,Ünlüsoy, S.,Eren, T.(2017).A combined goal programming—AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants.Renewable and Sustainable Energy Reviews,78,1410-1423.
17. Sammouri, W.,Côme, E.,Oukhellou, L.,Aknin, P.,Fonlladosa, C.-E.,Prendergast, K.(2012).Temporal association rule mining for the preventive diagnosis of onboard subsystems within floating train data framework.Proceedings of the 15th International IEEE Conference on Intelligent Transportation Systems,Anchorage, AK:
18. Shyjith, K.,Ilangkumaran, M.,Kumanan, S.(2008).Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry.Journal of Quality in Maintenance Engineering,14(4),375-386.
19. Velmurugan, R. S.,Dhingra, T.(2015).Maintenance strategy selection and its impact in maintenance function: a conceptual framework.International Journal of Operations & Production Management,35(12),1622-1661.
20. Wang, L.,Chu, J.,Wu, J.(2007).Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process.International Journal of Production Economics,107(1),151-163.
21. Xu, W.-X.,Fan, Y.-H.,Zhang, J.-X.(2014).Association rules algorithm and its application in the maintenance of the tunnel.International Journal of Simulation Modelling,13(4),458-471.
22. Yeh, C.-H.,Lin, M.-H.,Lin, C. H.,Yu, C.-E.,Chen, M. J.(2019).Machine learning for long cycle maintenance prediction of wind turbine.Sensors,19(7),1671.
23. Young, T.,Fehskens, M.,Pujara, P.,Burger, M.,Edwards, G.(2010).Utilizing data mining to influence maintenance actions.Proceedings of the 2010 IEEE AUTOTESTCON,Orlando, FL:
24. Zaim, S.,Turkyılmaz, A.,Acar, M. F.,Al-Turki, U.,Demirel, O. F.(2012).Maintenance strategy selection using AHP and ANP algorithms: a case study.Journal of Quality in Maintenance Engineering,18(1),16-29.