Simulation of Reliability Prediction Based on Multiple Factors for Spinning Machine

• Autorzy: Li Chen , Liu Rongxing , Pan Fukui

Identyfikatory

DOI

10.2478/aut-2019-0009

• Języki publikacji: EN

Abstrakty

EN

Reliability prediction of spinning machines can result in a time-saving and cost-saving development process with high reliability. Based on an analysis of failure times among systems and subsystems, a simulation method for reliability prediction of spinning machines is proposed by using the Monte Carlo simulation model. Firstly, factor weights are determined according to the fuzzy scoring and analytic hierarchy process. According to the status of reliability growth, growth coefficients are proposed based on reliability influencing factor weights and fuzzy scoring. To achieve the prediction of reliability distribution law, reliability index, and fault frequency, the reliability prediction model is constituted by combining the reliability growth coefficient and the Monte Carlo simulation model. Simulation results for spinning machines are obtained via the model thus built, which are confirmed with a practical example.

Słowa kluczowe

EN

spinning machine; reliability prediction; Monte-Carlo simulation; reliability growth

PL

przędzarka; prognoza niezawodności; symulacja Monte Carlo; wzrost niezawodności

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2020
• Tom: Vol. 20, no. 1
• Strony: 17--23
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Li Chen

• Textile Science Research Institute, Qingdao University, Qingdao, China 266071

autor

Liu Rongxing

• Textile Science Research Institute, Qingdao University, Qingdao, China 266071

autor

Pan Fukui

• Textile Science Research Institute, Qingdao University, Qingdao, China 266071

Bibliografia

• [1] Shi, J., (2011). Reliability prediction methods and application of large capacity generating units. Journal of Mechanical Engineering, 47(18), 165-172. (in Chinese)
• [2] Kuo, Y., Lin, K., (2010). Using neural network and decision tree for machine reliability prediction. International Journal of Advanced Manufacturing Technology, 50(9-12), 1243-1251.
• [3] Asri, Y. M., Azrulhisham, E. A., Dzuraidah, A. W., A. Shahrir, A. Shahrum, et al., (2011). Fatigue life reliability prediction of a stub axle using Monte Carlo simulation. International Journal of Automotive Technology, 12(5), 713-719.
• [4] Naess, A., Leira, B J, Batsevych O., (2009). System reliability analysis by enhanced Monte Carlo simulation. Structural Safety, 31(5), 349-355.
• [5] Dey, S, Sarmah, P. (1995). Estimation of parameters of a model of a complex repairable system. Microelectronics Reliability, 37(4), 673-676.
• [6] Yang, M, Lin, Z, He B., et al. (2003). Reliability prediction of mechanical product based on similarity. Machine Tool & Hydraulics, (1), 63-65. (in Chinese)
• [7] Gaver, D. P. (1963). Time to failure and availability of paralleled system with repair. IEEE Transactions on Reliability, 12, 30-38.
• [8] Cox, D. R. (1955). The analysis of non-Markovian stochastic processes by the inclusion of supplementary variables. Cambridge Phil, 51, 433-441.
• [9] Yang, Z., Hao, Q, Chen F, et al. (2011). A comprehensive fuzzy reliability allocation method of NC machine tools based on interval analysis. Journal of Beijing University of Technology, 37(3), 321-329. (in Chinese)
• [10] Wang, S., Li, S, Zhou, J., Li, Q., Kang, L., (2012). Reliability allocation for CNC machine based on improved fuzzy analytic hierarchy process. Advances in Information Sciences and Service Sciences, 4(1), 320-327.
• [11] Peng, B., Zhao, J., Sun, Q., (2005). Reliability allocation method for complex system using AHP. Electronic Product Reliability and Environmental Testing, 23(6), 58-62. (in Chinese)
• [12] Zhang, H., Jia, Y., Zhou, G., (2007). Time between failures model and failure analysis of CNC system. Journal of Harbin Institute of Technology, 14(2), 197-201.
• [13] Shen, G., Chen, B., Zhang, Y., et al. (2011). Reliability model for subsystems of CNC machine tool with small samples. Journal of Chongqing University, 34(8), 55-59.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).