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On the use of machine learning methods to predict component reliability from data-driven industrial case studies

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Abstract

The reliability estimation of engineered components is fundamental for many optimization policies in a production process. The main goal of this paper is to study how machine learning models can fit this reliability estimation function in comparison with traditional approaches (e.g., Weibull distribution). We use a supervised machine learning approach to predict this reliability in 19 industrial components obtained from real industries. Particularly, four diverse machine learning approaches are implemented: artificial neural networks, support vector machines, random forest, and soft computing methods. We evaluate if there is one approach that outperforms the others when predicting the reliability of all the components, analyze if machine learning models improve their performance in the presence of censored data, and finally, understand the performance impact when the number of available inputs changes. Our experimental results show the high ability of machine learning to predict the component reliability and particularly, random forest, which generally obtains high accuracy and the best results for all the cases. Experimentation confirms that all the models improve their performance when considering censored data. Finally, we show how machine learning models obtain better prediction results with respect to traditional methods when increasing the size of the time-to-failure datasets.

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References

  1. Birolini A (2007) Reliability engineering, vol 5. Springer, Berlin

    Google Scholar 

  2. Shi H, Zeng J (2016) Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Comput Ind Eng 93:192–204

    Article  Google Scholar 

  3. Luo M, Yan HC, Hu B, Zhou JH, Pang CK (2015) A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Comput Ind Eng 85:414–422

    Article  Google Scholar 

  4. Sun Y, Ma L, Mathew J (2009) Failure analysis of engineering systems with preventive maintenance and failure interactions. Comput Ind Eng 57(2):539–549

    Article  Google Scholar 

  5. Rodger J A, George JA (2017) Triple bottom line accounting for optimizing natural gas sustainability: a statistical linear programming fuzzy ILOWA optimized sustainment model approach to reducing supply chain global cybersecurity vulnerability through information and communications technology. J Clean Prod 142:1931–1949

    Article  Google Scholar 

  6. Ebeling CE (2010) An introduction to reliability and maintainability engineering. Waveland Press, Long Grove

    Google Scholar 

  7. O’Connor P, Kleyner A (2011) Practical reliability engineering. Wiley, Hoboken

    Book  Google Scholar 

  8. Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63(1):135–149

    Article  Google Scholar 

  9. Crocker J, Kumar UD (2000) Age-related maintenance versus reliability centred maintenance: a case study on aero-engines. Reliab Eng Syst Saf 67(2):113–118

    Article  Google Scholar 

  10. Manzini R, Regattieri A, Pham H, Ferrari E (2009) Maintenance for industrial systems. Springer Science & Business Media, Berlin

    MATH  Google Scholar 

  11. Nakagawa T (2006) Maintenance theory of reliability. Springer Science & Business Media, Berlin

    Google Scholar 

  12. das Chagas Moura M, Zio E, Lins ID, Droguett E (2011) Failure and reliability prediction by support vector machines regression of time series data. Reliab Eng Syst Saf 96(11):1527–1534

    Article  Google Scholar 

  13. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

  14. Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco

    MATH  Google Scholar 

  15. Amjady N, Ehsan M (1999) Evaluation of power systems reliability by an artificial neural network. IEEE Trans Power Syst 14(1):287–292

    Article  Google Scholar 

  16. Chatterjee S, Bandopadhyay S (2012) Reliability estimation using a genetic algorithm-based artificial neural network: an application to a load-haul-dump machine. Expert Syst Appl 39(12):10,943–10,951

    Article  Google Scholar 

  17. Hong WC, Pai PF (2006) Predicting engine reliability by support vector machines. Int J Adv Manuf Technol 28(1–2):154–161

    Article  Google Scholar 

  18. Gokulachandran J, Mohandas K (2015) Comparative study of two soft computing techniques for the prediction of remaining useful life of cutting tools. J Intell Manuf 26(2):255–268

    Article  Google Scholar 

  19. Wu X, Zhu Z, Fan S, Su X (2016) Failure and reliability prediction of engine systems using iterated nonlinear filters based state-space least square support vector machine method. Optik Int J Light Electron Opt 127 (3):1491–1496

    Article  Google Scholar 

  20. Regattieri A, Manzini R, Battini D (2010) Estimating reliability characteristics in the presence of censored data: a case study in a light commercial vehicle manufacturing system. Reliab Eng Syst Saf 95(10):1093–1102

    Article  Google Scholar 

  21. Kumar UD, Crocker J, Knezevic J, El-Haram M (2012) Reliability, maintenance and logistic support: a life cycle approach. Springer, Berlin

    Google Scholar 

  22. Meeker WQ, Escobar LA (1998) Statistical methods for reliability data, vol 314. Wiley-Interscience, Hoboken

    MATH  Google Scholar 

  23. Nelson WB (2009) Accelerated testing: statistical models, test plans, and data analysis, vol 344. Wiley, Hoboken

    Google Scholar 

  24. Jardine AK, Tsang AH (2013) Maintenance, replacement, and reliability: theory and applications. CRC Press, Boca Raton

    MATH  Google Scholar 

  25. Bontempi G, Taieb SB, Le Borgne YA (2013) Machine learning strategies for time series forecasting. In: Business intelligence. Springer, Berlin, pp 62–77

  26. Vanini ZS, Khorasani K, Meskin N (2014) Fault detection and isolation of a dual spool gas turbine engine using dynamic neural networks and multiple model approach. Inf Sci 259:234–251

    Article  Google Scholar 

  27. Ogaji SO, Singh R (2003) Advanced engine diagnostics using artificial neural networks. Appl Soft Comput 3(3):259–271

    Article  Google Scholar 

  28. Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574

    Article  Google Scholar 

  29. Liu MC, Kuo W, Sastri T (1995) An exploratory study of a neural network approach for reliability data analysis. Qual Reliab Eng Int 11(2):107–112

    Article  Google Scholar 

  30. Alsina EF, Cabri G, Regattieri A (2016) A neural network approach to find the cumulative failure distribution: modeling and experimental evidence. Qual Reliab Eng Int 32(2):167–579

    Article  Google Scholar 

  31. Ho S, Xie M, Goh T (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Comput Ind Eng 42(2):371–375

    Article  Google Scholar 

  32. Xu K, Xie M, Tang LC, Ho S (2003) Application of neural networks in forecasting engine systems reliability. Appl Soft Comput 2(4):255–268

    Article  Google Scholar 

  33. Fink O, Zio E, Weidmann U (2014) Predicting component reliability and level of degradation with complex-valued neural networks. Reliab Eng Syst Saf 121:198–206

    Article  Google Scholar 

  34. Lins ID, Moura MdC, Zio E, Droguett EL (2012) A particle swarm-optimized support vector machine for reliability prediction. Qual Reliab Eng Int 28(2):141–158

    Article  Google Scholar 

  35. Chen KY (2007) Forecasting systems reliability based on support vector regression with genetic algorithms. Reliab Eng Syst Saf 92(4):423–432

    Article  Google Scholar 

  36. Pai PF, Lin KP (2006) Application of hybrid learning neural fuzzy systems in reliability prediction. Qual Reliab Eng Int 22(2):199–211

    Article  Google Scholar 

  37. Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84

    Article  Google Scholar 

  38. Draper NR, Smith H (2014) Applied regression analysis. Wiley, Hoboken

    MATH  Google Scholar 

  39. Marsland S (2015) Machine learning: an algorithmic perspective. CRC Press, Boca Raton

    Google Scholar 

  40. Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle Rive

    Google Scholar 

  41. Alzghoul A, Löfstrand M, Backe B (2012) Data stream forecasting for system fault prediction. Comput Ind Eng 62(4):972–978

    Article  Google Scholar 

  42. Rustagi J (1994) Optimization techniques in statistics. Academic Press, Cambridge

    MATH  Google Scholar 

  43. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  MathSciNet  Google Scholar 

  44. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  45. Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems. Springer, Berlin, pp 1–15

  46. Kuncheva L (2001) Combining classifiers: soft computing solutions. In: Pal S, Pal A (eds) Pattern recognition. From classical to modern approaches. World Scientific, Singapore, pp 427–451

  47. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  48. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140

    MATH  Google Scholar 

  49. Haykin S (1998) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall PTR, NJ

    MATH  Google Scholar 

  50. Moller F (1990) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  51. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  Google Scholar 

  52. Yager RR, Filev DP (1994) Essentials of fuzzy modeling and control. Wiley-Interscience, New York

    Google Scholar 

  53. Casillas J, Cordon O, Herrera F, Magdalena L (2003) Interpretability issues in fuzzy modeling. Springer, Berlin

    Book  MATH  Google Scholar 

  54. Alonso JM, Magdalena L, González-Rodríguez G (2009) Looking for a good fuzzy system interpretability index: an experimental approach. Int J Approx Reason 51:115–134

    Article  MathSciNet  Google Scholar 

  55. Cordón O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore

    Book  MATH  Google Scholar 

  56. Cordón O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141(1):5–31

    Article  MathSciNet  MATH  Google Scholar 

  57. Herrera F (2008) Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol Intell 1:27–46

    Article  Google Scholar 

  58. Sánchez L, Couso I, Corrales J (2001) Combining GP operators with SA search to evolve fuzzy rule based classifiers. Inf Sci 136(1-4):175–191

    Article  MATH  Google Scholar 

  59. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Sci 220(4598):671–680

    Article  MathSciNet  MATH  Google Scholar 

  60. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, vol 1. MIT Press, Cambridge

    MATH  Google Scholar 

  61. Ferrari E, Pareschi A, Regattieri A, Persona A (2002) TPM: situation and procedure for a soft introduction in Italian factories. TQM Mag 14(6):350–358

    Article  Google Scholar 

  62. Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53(282):457–481

    Article  MathSciNet  MATH  Google Scholar 

  63. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th international joint conference on artificial intelligence, IJCAI’95, vol 2. Morgan Kaufmann Publishers Inc., San Francisco, pp 1137–1143

  64. Alcalá-Fdez J, Sanchez L, Garcia S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas V M et al (2009) Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comp 13(3):307–318

    Article  Google Scholar 

  65. Wang ZM, Yang JG (2012) Numerical method for Weibull generalized renewal process and its applications in reliability analysis of nc machine tools. Comput Ind Eng 63(4):1128–1134

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Physics, Mathematics and Informatics, University of Modena and Reggio Emilia, 41125, Modena, Italy

    Emanuel F. Alsina

  2. RØD Brand Consultants, 28001, Madrid, Spain

    Manuel Chica

  3. School of Electrical Engineering and Computing, The University of Newcastle, Callaghan, NSW, 2308, Australia

    Manuel Chica

  4. Novelti, 28012, Madrid, Spain

    Krzysztof Trawiński

  5. Department of Industrial Engineering, University of Bologna, 40136, Bologna, Italy

    Alberto Regattieri

Authors
  1. Emanuel F. Alsina
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  2. Manuel Chica
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  3. Krzysztof Trawiński
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  4. Alberto Regattieri
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Correspondence to Manuel Chica.

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Alsina, E.F., Chica, M., Trawiński, K. et al. On the use of machine learning methods to predict component reliability from data-driven industrial case studies. Int J Adv Manuf Technol 94, 2419–2433 (2018). https://doi.org/10.1007/s00170-017-1039-x

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  • DOI: https://doi.org/10.1007/s00170-017-1039-x

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