رویکرد جدید آنالیز تخریب سیستم های چندجزئی مبتنی بر روابط تابعی با در نظر گرفتن وابستگی تصادفی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه صنعتی مالک اشتر، دانشکده مدیریت و مهندسی صنایع

2 دانشگاه صنعتی مالک اشتر، دانشکده هوافضا

چکیده

امروزه آنالیز تخریب یکی از رویکردهای بسیار با اهمیت در ارزیابی قابلیت اطمینان سیستم های چندجزئی است. بررسی ادبیات نشان می‌دهد آنالیز تخریب سیستم های چند جزئی در تحقیقات مختلفی مورد بررسی قرار گرفته است، اما رویکردی که در آن بین فرآیندهای تخریب اجزا سیستم رابطه پروفایلی وجود دارد، تاکنون مورد توجه قرار نگرفته است. هنگامی که بین فرآیندهای تخریب یک یا چند جزء رابطه تابعی وجود داشته باشد، در ادبیات کنترل فرآیند آماری به آن پروفایل گفته می‌شود. هدف از این مطالعه، ارائه رویکردی کارآمد جهت پیش بینی و ارزیابی تغیییرپذیری فرآیندهای تخریب در شرایط وجود پروفایل چند متغیره تحت شرایط وابستگی تصادفی است. در واقع رویکرد پیشنهادی امکان پیش بینی و ارزیابی تغییرپذیری فرآیندهای تخریب در سطح اجزاء و سیستم را ارائه می نماید. در این مقاله به منظور ارزیابی رویکرد پیشنهادی، از مجموعه داده های یک سیستم چندجزئی با ساختار 2 از 3 استفاده شده است.

کلیدواژه‌ها

عنوان مقاله [English]

A new Degradation analysis approach for multi-component systems based on functional relationships considering stochastic dependency

نویسندگان [English]

  • karim Atashgar 1
  • Mehdi Karbasian 1
  • Mostafa Khazaee 2
  • Majid Abbasi 1
1 Faculty of Industrial Engineering, Malek Ashtar University of Technology
2 Faculty of Aerospace, Malek Ashtar University of Technology
چکیده [English]

Today, degradation analysis is one of the most important approaches in evaluating the reliability of multi-component systems. As it is clear, improving the performance of real systems requires the use of efficient and predictable approaches to analyze degradation with considering the interaction of system degradation processes on each other. The literature review shows that degradation analysis of multi-component systems has been investigated in various researches, but the approach in which there is a profile relationship between the degradation processes of the system components has not been considered so far. When there is a functional relationship between the degradation processes of one or more components, it is called a profile in the statistical process control literature. The aim of this study is to provide an efficient approach to predict and evaluate the variability of degradation processes in the presence of multivariate profiles under the conditions of stochastic dependence. In fact, the proposed approach offers the possibility of predicting and evaluating the variability of degradation processes at the component and system level. In this paper, in order to evaluate the proposed approach, the data set of a multi-component system with a structure of 2 out of 3 has been used. The results show the effectiveness of the proposed approach.

کلیدواژه‌ها [English]

  • Degradation Analysis
  • Stochastic Dependency
  • functional relationships
  • Multi-Component Systems

مراجع

Alaswad, S., & Xiang, Y. (2017). A review on condition-based maintenance optimization models for stochastically deteriorating system. Reliability engineering & system safety, 157, 54-63.
Wang, Y., Li, X., Chen, J., & Liu, Y. (2022). A condition-based maintenance policy for multi-component systems subject to stochastic and economic dependencies. Reliability Engineering & System Safety, 219, 108174.
Kong, X., Yang, J., & Li, L. (2022). Reliability analysis for multi-component systems considering stochastic dependency based on factor analysis. Mechanical Systems and Signal Processing, 169, 108754.
Shen, J., Elwany, A., & Cui, L. (2018). Reliability analysis for multi-component systems with degradation interaction and categorized shocks. Applied Mathematical Modelling, 56, 487-500.
Keizer, M. C. O., Flapper, S. D. P., & Teunter, R. H. (2017). Condition-based maintenance policies for systems with multiple dependent components: A review. European Journal of Operational Research, 261(2), 405-420.
Zhang, N., Fouladirad, M., & Barros, A. (2019). Reliability-based measures and prognostic analysis of a K-out-of-N system in a random environment. European Journal of Operational Research, 272(3), 1120-1131.
Zhang, N., Fouladirad, M., Barros, A., & Zhang, J. (2020). Condition-based maintenance for a K-out-of-N deteriorating system under periodic inspection with failure dependence. European Journal of Operational Research, 287(1), 159-167.
Van Horenbeek, A., & Pintelon, L. (2013). A dynamic predictive maintenance policy for complex multi-component systems. Reliability engineering & system safety, 120, 39-50.
Feng, Q., Jiang, L., & Coit, D. W. (2016). Reliability analysis and condition-based maintenance of systems with dependent degrading components based on thermodynamic physics-of-failure. The International Journal of Advanced Manufacturing Technology, 86, 913-923.
Song, S., Coit, D. W., & Feng, Q. (2016). Reliability analysis of multiple-component series systems subject to hard and soft failures with dependent shock effects. IIE Transactions, 48(8), 720-735.
Fang, G., & Pan, R. (2021). On multivariate copula modeling of dependent degradation processes. Computers & Industrial Engineering, 159, 107450.
Fang, G., & Pan, R. (2021). On multivariate copula modeling of dependent degradation processes. Computers & Industrial Engineering, 159, 107450.
Liu, B., Pandey, M. D., Wang, X., & Zhao, X. (2021). A finite-horizon condition-based maintenance policy for a two-unit system with dependent degradation processes. European Journal of Operational Research, 295(2), 705-717.
Bian, L., & Gebraeel, N. (2014). Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions. Iie Transactions, 46(5), 470-482.
Wang, P., & Coit, D. W. (2004, January). Reliability prediction based on degradation modeling for systems with multiple degradation measures. In Annual Symposium Reliability and Maintainability, 2004-RAMS (pp. 302-307). IEEE.
Xu, J., Liang, Z., Li, Y. F., & Wang, K. (2021). Generalized condition-based maintenance optimization for multi-component systems considering stochastic dependency and imperfect maintenance. Reliability Engineering & System Safety, 211, 107592.
Fang, G., Pan, R., & Hong, Y. (2020). Copula-based reliability analysis of degrading systems with dependent failures. Reliability Engineering & System Safety, 193, 106618.
Bian, L., & Gebraeel, N. (2014). Stochastic framework for partially degradation systems with continuous component degradation‐rate‐interactions. Naval Research Logistics (NRL), 61(4), 286-303.
Rasmekomen, N., & Parlikad, A. K. (2014). Optimising maintenance of multi-component systems with degradation interactions. IFAC Proceedings Volumes, 47(3), 7098-7103.
Chalabi, N., Dahane, M., Beldjilali, B., & Neki, A. (2016). Optimisation of preventive maintenance grouping strategy for multi-component series systems: Particle swarm based approach. Computers & Industrial Engineering, 102, 440-451.
Nguyen, K. A., Do, P., & Grall, A. (2015). Multi-level predictive maintenance for multi-component systems. Reliability engineering & system safety, 144, 83-94.
Xu, M., Jin, X., Kamarthi, S., & Noor-E-Alam, M. (2018). A failure-dependency modeling and state discretization approach for condition-based maintenance optimization of multi-component systems. Journal of manufacturing systems, 47, 141-152.
Rodriguez-Picon, L. A. (2017). Reliability assessment for systems with two performance characteristics based on gamma processes with marginal heterogeneous random effects. Eksploatacja i Niezawodność, 19(1), 8-18.
Niu, H., Zeng, J., Shi, H., Zhang, X., & Liang, J. (2023). Degradation modeling and remaining useful life prediction for a multi-component system with stochastic dependence. Computers & Industrial Engineering, 175, 108889.
Song, S., Coit, D. W., Feng, Q., & Peng, H. (2014). Reliability analysis for multi-component systems subject to multiple dependent competing failure processes. IEEE Transactions on Reliability, 63(1), 331-345.
Dinh, D. H., Do, P., & Iung, B. (2020). Degradation modeling and reliability assessment for a multi-component system with structural dependence. Computers & Industrial Engineering, 144, 106443.
Xu, L., Chen, Y., Briand, F., Zhou, F., & Givanni, M. (2017). Reliability measurement for multistate manufacturing systems with failure interaction. Procedia CIRP, 63, 242-247.
Dong, Q., Cui, L., & Si, S. (2020). Reliability and availability analysis of stochastic degradation systems based on bivariate Wiener processes. Applied Mathematical Modelling, 79, 414-433.
Dong, W., Liu, S., Bae, S. J., & Cao, Y. (2021). Reliability modelling for multi-component systems subject to stochastic deterioration and generalized cumulative shock damages. Reliability Engineering & System Safety, 205, 107260.
Meango, T. J. M., & Ouali, M. S. (2019). Failure interaction models for multicomponent systems: a comparative study. SN Applied Sciences, 1, 1-25.
Noorossana, R., Eyvazian, M., & Vaghefi, A. (2010). Phase II monitoring of multivariate simple linear profiles. Computers & Industrial Engineering, 58(4), 563-570.
Eyvazian, M., Noorossana, R., Saghaei, A., & Amiri, A. (2011). Phase II monitoring of multivariate multiple linear regression profiles. Quality and Reliability Engineering International, 27(3), 281-296.
Atashgar, K., & Zargarabadi, O. A. (2017). Monitoring multivariate profile data in plastic parts manufacturing industries: An intelligently data processing. Journal of Industrial Information Integration, 8, 38-48.
Atashgar, K., Amiri, A., & Nejad, M. K. (2015). Monitoring Allan variance nonlinear profile using artificial neural network approach. International Journal of Quality Engineering and Technology, 5(2), 162-177.
Chiang, J. Y., Lio, Y. L., & Tsai, T. R. (2017). MEWMA control chart and process capability indices for simple linear profiles with within‐profile autocorrelation. Quality and Reliability Engineering International, 33(5), 1083-1094.
Noor‐ul‐Amin, M., Arshad, A., & Hanif, M. (2022). A function based adaptive EWMA mean monitoring control chart. Quality and Reliability Engineering International, 38(1), 248-263.
Tang, A., Castagliola, P., Sun, J., & Hu, X. (2018). The effect of measurement errors on the adaptive EWMA chart. Quality and Reliability Engineering International, 34(4), 609-630.
Noor‐ul‐Amin, M., & Noor, S. (2021). An adaptive EWMA control chart for monitoring the process mean in Bayesian theory under different loss functions. Quality and Reliability Engineering International, 37(2), 804-819.
 
نشریه علمی پژوهشی مهندسی و مدیریت کیفیت
دوره 13، شماره 1
خرداد 1401
صفحه 1-16

فایل ها

هم رسانی

ارجاع به این مقاله

آمار