Predictive Maintenance Planning Using a Hybrid ARIMA-ANN Model

Abstract

Predicting machine faults is crucial for maintaining operational efficiency in industrial settings, minimizing unplanned downtime, and ensuring customer satisfaction. Fault prediction helps identify faults and create maintenance schedules. Maintenance planning involves strategically scheduling activities to ensure the continuous operational efficiency of systems. This study focuses on reducing unplanned downtime in a food company by developing a predictive maintenance plan through machine fault prediction. Artificial Neural Networks (ANNs) are excellent in handling non-linear models, while the ARIMA model is adequate for linear models. However, real-world data often contains linear and non-linear elements, requiring hybrid models for improved accuracy. This study employs ARIMA, ANNs, and a Hybrid ARIMA-ANN model. The dataset is individually modelled using each approach. Using a 3-month machine fault dataset, predictive values for machine fault times are generated and statistically evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The findings indicate that the hybrid model outperforms both ARIMA and ANN models. The food company can significantly reduce unplanned downtime and ensure operational efficiency using a hybrid model. Predictive maintenance planning can help the food company save costs and maintain a competitive edge in the market.

Keywords

• Reliability
• ARIMA
• ANN
• Time Series Analysis
• Predictive maintenance

References

1. [1] J. Leukel, J. González, and M. Riekert, “Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review,” J. Manuf. Syst., vol. 61, no. September, pp. 87–96, 2021, doi: 10.1016/j.jmsy.2021.08.012.
2. [2] D. M. Louit, R. Pascual, and A. K. S. Jardine, “A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data,” Reliab. Eng. Syst. Saf., vol. 94, no. 10, pp. 1618–1628, Oct. 2009, doi: 10.1016/J.RESS.2009.04.001.
3. [3] M. Zufle, J. Agne, J. Grohmann, I. Dortoluk, and S. Kounev, “A Predictive Maintenance Methodology: Predicting the Time-to-Failure of Machines in Industry 4.0,” in 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), IEEE, Jul. 2021, pp. 1–8. doi: 10.1109/INDIN45523.2021.9557387.
4. [4] E. F. Alsina, M. Chica, K. Trawiński, and A. Regattieri, “On the use of machine learning methods to predict component reliability from data-driven industrial case studies,” Int. J. Adv. Manuf. Technol., vol. 94, no. 5–8, pp. 2419–2433, Feb. 2018, doi: 10.1007/s00170-017-1039-x.
5. [5] W. Zhao, T. Tao, and E. Zio, “System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection,” Appl. Soft Comput., vol. 30, pp. 792–802, May 2015, doi: 10.1016/J.ASOC.2015.02.026.
6. [6] M. Baptista, S. Sankararaman, I. P. de Medeiros, C. Nascimento, H. Prendinger, and E. M. P. Henriques, “Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling,” Comput. Ind. Eng., vol. 115, no. September 2017, pp. 41–53, Jan. 2018, doi: 10.1016/j.cie.2017.10.033.
7. [7] M. Fernandes, A. Canito, J. M. Corchado, and G. Marreiros, “Fault Detection Mechanism of a Predictive Maintenance System Based on Autoregressive Integrated Moving Average Models,” in Advances in Intelligent Systems and Computing, vol. 1003, Springer International Publishing, 2020, pp. 171–180. doi: 10.1007/978-3-030-23887-2_20.
8. [8] M. Ángel, N. Álvarez, J. Carpio Ibáñez, and C. Sancho De Mingo, “Reliability Assessment of Repairable Systems Using Simple Regression Models,” Int. J. Math. Eng. Manag. Sci., vol. 6, no. 1, pp. 180–192, 2021, doi: 10.33889/IJMEMS.2021.6.1.011.

9. [9] H. İ. Ayaz and Z. Ozturk, Kamisli, “Shilling Attack Detection with One Class Support Vector Machines,” Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilim. Derg., vol. 5, no. 2, pp. 246–256, Dec. 2023, doi: 10.47112/neufmbd.2023.22.
10. [10] S. Fernandes, M. Antunes, A. R. Santiago, J. P. Barraca, D. Gomes, and R. L. Aguiar, “Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance,” Information, vol. 11, no. 4, p. 208, Apr. 2020, doi: 10.3390/info11040208.
11. [11] G. Makridis, D. Kyriazis, and S. Plitsos, “Predictive maintenance leveraging machine learning for time-series forecasting in the maritime industry,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), IEEE, Sep. 2020, pp. 1–8. doi: 10.1109/ITSC45102.2020.9294450.
12. [12] S. Ayvaz and K. Alpay, “Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time,” Expert Syst. Appl., vol. 173, no. September 2020, p. 114598, Jul. 2021, doi: 10.1016/j.eswa.2021.114598.
13. [13] M. Bevilacqua, M. Braglia, M. Frosolini, and R. Montanari, “Failure rate prediction with artificial neural networks,” J. Qual. Maint. Eng., vol. 11, no. 3, pp. 279–294, Sep. 2005, doi: 10.1108/13552510510616487.
14. [14] P. Samaranayake and S. Kiridena, “Aircraft maintenance planning and scheduling: An integrated framework,” J. Qual. Maint. Eng., vol. 18, no. 4, pp. 432–453, Oct. 2012, doi: 10.1108/13552511211281598.
15. [15] C. Guedes Soares, Ed., Safety and Reliability of Industrial Products, Systems and Structures. CRC Press, 2010. doi: 10.1201/b10572.
16. [16] S. Kolidakis, G. Botzoris, V. Profillidis, and P. Lemonakis, “Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis,” Econ. Anal. Policy, vol. 64, pp. 159–171, Dec. 2019, doi: 10.1016/J.EAP.2019.08.002.
17. [17] G. Aydin, I. Karakurt, and C. Hamzacebi, “Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting,” Int. J. Adv. Manuf. Technol., vol. 75, no. 9–12, pp. 1321–1330, Dec. 2014, doi: 10.1007/s00170-014-6211-y.
18. [18] P. G. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159–175, Jan. 2003, doi: 10.1016/S0925-2312(01)00702-0.
19. [19] A. Bahadır and C. Aydin, “Talaşlı İmalat Sektöründe Zaman Serileri Kullanarak Üretim Etkililiğinin Tahmini,” Bilişim Teknol. Derg., vol. 11, no. 4, pp. 407–416, Oct. 2018, doi: 10.17671/gazibtd.383339.
20. [20] A. Safari and M. Davallou, “Oil price forecasting using a hybrid model,” Energy, vol. 148, pp. 49–58, Apr. 2018, doi: 10.1016/j.energy.2018.01.007.
21. [21] J.-J. Wang, J.-Z. Wang, Z.-G. Zhang, and S.-P. Guo, “Stock index forecasting based on a hybrid model,” Omega, vol. 40, no. 6, pp. 758–766, Dec. 2012, doi: 10.1016/j.omega.2011.07.008.
22. [22] Z. Li, Y. Wang, and K.-S. Wang, “Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario,” Adv. Manuf., vol. 5, no. 4, pp. 377–387, Dec. 2017, doi: 10.1007/s40436-017-0203-8.
23. [23] L. Zuo, L. Zhang, Z.-H. Zhang, X.-L. Luo, and Y. Liu, “A spiking neural network-based approach to bearing fault diagnosis,” J. Manuf. Syst., vol. 61, pp. 714–724, Oct. 2021, doi: 10.1016/j.jmsy.2020.07.003.
24. [24] G. Scalabrini Sampaio, A. R. de A. Vallim Filho, L. Santos da Silva, and L. Augusto da Silva, “Prediction of Motor Failure Time Using An Artificial Neural Network,” Sensors, vol. 19, no. 19, p. 4342, Oct. 2019, doi: 10.3390/s19194342.
25. [25] J. Ben Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, and F. Fnaiech, “Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals,” Appl. Acoust., vol. 89, pp. 16–27, Mar. 2015, doi: 10.1016/J.APACOUST.2014.08.016.
26. [26] A. K. Mahamad, S. Saon, and T. Hiyama, “Predicting remaining useful life of rotating machinery based artificial neural network,” Comput. Math. with Appl., vol. 60, no. 4, pp. 1078–1087, Aug. 2010, doi: 10.1016/J.CAMWA.2010.03.065.
27. [27] J. Zhang, P. Wang, R. Yan, and R. X. Gao, “Deep Learning for Improved System Remaining Life Prediction,” Procedia CIRP, vol. 72, pp. 1033–1038, Jan. 2018, doi: 10.1016/J.PROCIR.2018.03.262.
28. [28] W. J. Lee, H. Wu, H. Yun, H. Kim, M. B. G. Jun, and J. W. Sutherland, “Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data,” Procedia CIRP, vol. 80, pp. 506–511, Jan. 2019, doi: 10.1016/J.PROCIR.2018.12.019.
29. [29] A. Paithankar and S. Chatterjee, “Forecasting time-to-failure of machine using hybrid Neuro-genetic algorithm – a case study in mining machinery,” Int. J. Mining, Reclam. Environ., vol. 32, no. 3, pp. 182–195, Apr. 2018, doi: 10.1080/17480930.2016.1262499.
30. [30] D. Yang, X. Hai, Y. Ren, J. Cui, K. Li, and S. Zeng, “A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model,” Asian J. Control, vol. 25, no. 1, pp. 418–432, Jan. 2023, doi: 10.1002/ASJC.2802.
31. [31] S. Xu, H. K. Chan, and T. Zhang, “Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach,” Transp. Res. Part E Logist. Transp. Rev., vol. 122, pp. 169–180, Feb. 2019, doi: 10.1016/J.TRE.2018.12.005.
32. [32] K. Celikmih, O. Inan, and H. Uguz, “Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method,” Sci. Program., vol. 2020, 2020, doi: 10.1155/2020/8616039.
33. [33] S. Dindarloo, “Reliability forecasting of a load-haul-dump machine: A comparative study of ARIMA and neural networks,” Qual. Reliab. Eng. Int., vol. 32, no. 4, pp. 1545–1552, Jun. 2016, doi: 10.1002/qre.1844.
34. [34] H. Liu, “Application of industrial Internet of things technology in fault diagnosis of food machinery equipment based on neural network,” Soft Comput., vol. 27, no. 13, pp. 9001–9018, Jul. 2023, doi: 10.1007/s00500-023-08412-5.
35. [35] I. Setiawan, A. Bahrudin, M. M. Arifin, W. I. Fipiana, and V. Lusia, “Analysis of Preventive Maintenance and Breakdown Maintenance on Production Achievement in the Food Seasoning Industry,” OPSI, vol. 14, no. 2, pp. 253–261, Dec. 2021, doi: 10.31315/OPSI.V14I2.5540.
36. [36] V. M. Vargas, R. Rosati, C. Hervás-Martínez, A. Mancini, L. Romeo, and P. A. Gutiérrez, “A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data,” Eng. Appl. Artif. Intell., vol. 123, p. 106463, Aug. 2023, doi: 10.1016/j.engappai.2023.106463.
37. [37] D. L. Rivera, M. R. Scholz, C. Bühl, M. Krauss, and K. Schilling, “Is Big Data About to Retire Expert Knowledge? A Predictive Maintenance Study,” IFAC-PapersOnLine, vol. 52, no. 24, pp. 1–6, Jan. 2019, doi: 10.1016/j.ifacol.2019.12.364.
38. [38] N. Aktepe, “Toplam verimli bakım ve bir imalat işletmesinde uygulaması,” Akdeniz Üniversitesi, 2007. Accessed: Mar. 31, 2024. [Online]. Available: http://acikerisim.akdeniz.edu.tr/xmlui/handle/123456789/5011
39. [39] M. A. Mansor, A. Ohsato, and S. Sulaiman, “Knowledge Management for Maintenance Activities in the Manufacturing Sector,” Int. J. Automot. Mech. Eng., vol. 5, no. 1, pp. 612–621, Jun. 2012, doi: 10.15282/ijame.5.2012.7.0048.
40. [40] R. Abbassi, J. Bhandari, F. Khan, V. Garaniya, and S. Chai, “Developing a Quantitative Risk-based Methodology for Maintenance Scheduling Using Bayesian Network,” Chem. Eng. Trans., vol. 48, pp. 235–240, Apr. 2016, doi: 10.3303/CET1648040.
41. [41] A. Medeiros, A. Sartori, S. F. Stefenon, L. H. Meyer, and A. Nied, “Comparison of artificial intelligence techniques to failure prediction in contaminated insulators based on leakage current,” J. Intell. Fuzzy Syst., vol. 42, no. 4, pp. 3285–3298, Mar. 2022, doi: 10.3233/JIFS-211126.
42. [42] R. Kang et al., “A method of online anomaly perception and failure prediction for high-speed automatic train protection system,” Reliab. Eng. Syst. Saf., vol. 226, p. 108699, Oct. 2022, doi: 10.1016/j.ress.2022.108699.
43. [43] B. Soylu, H. Yiğiter, V. Sarıkaya, Z. Sandıkçı, and A. Utku, “Kestirimci bakim planlama için makine öğrenmesi temelli bir karar destek sistemi ve bir uygulama,” Veriml. Derg., vol. 0, no. Dijital Dönüşüm ve Verimlilik, pp. 48–66, Jan. 2022, doi: 10.51551/verimlilik.988104.
44. [44] S. PERÇİN and S. ÇAKIR, “Çok Kriterli Karar Verme Teknikleriyle Lojistik Firmalarında Performans Ölçümü,” Ege Akad. Bakis (Ege Acad. Rev., vol. 13, no. 4, pp. 449–449, 2013, doi: 10.21121/eab.2013418079.
45. [45] M. Cakir, M. A. Guvenc, and S. Mistikoglu, “The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system,” Comput. Ind. Eng., vol. 151, p. 106948, Jan. 2021, doi: 10.1016/j.cie.2020.106948.
46. [46] A. Birolini, Reliability Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. doi: 10.1007/978-3-662-05409-3.
47. [47] S. Sajid, A. Haleem, S. Bahl, M. Javaid, T. Goyal, and M. Mittal, “Data science applications for predictive maintenance and materials science in context to Industry 4.0,” Mater. Today Proc., vol. 45, pp. 4898–4905, Jan. 2021, doi: 10.1016/J.MATPR.2021.01.357.
48. [48] G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control. John Wiley & Sons, 2015.
49. [49] Q. Jiang, L. Zhu, C. Shu, and V. Sekar, “An efficient multilayer RBF neural network and its application to regression problems,” Neural Comput. Appl., vol. 34, no. 6, pp. 4133–4150, Mar. 2022, doi: 10.1007/s00521-021-06373-0.
50. [50] P. H. Borghi, O. Zakordonets, and J. P. Teixeira, “A COVID-19 time series forecasting model based on MLP ANN,” Procedia Comput. Sci., vol. 181, pp. 940–947, Jan. 2021, doi: 10.1016/J.PROCS.2021.01.250.
51. [51] A. Di Piazza, M. C. Di Piazza, G. La Tona, and M. Luna, “An artificial neural network-based forecasting model of energy-related time series for electrical grid management,” Math. Comput. Simul., vol. 184, pp. 294–305, Jun. 2021, doi: 10.1016/J.MATCOM.2020.05.010.
52. [52] T. Sarı, S. R. Şensoy, A. E. Nurbaki, and İ. A. Ağaç, “Yapay Sinir Ağları Yaklaşımı ile Talep Tahmini: Madeni Eşya İmalat Sektöründe Bir Uygulama,” Veriml. Derg., vol. 57, no. 4, pp. 701–718, Oct. 2023, doi: 10.51551/VERIMLILIK.1327524.
53. [53] L. Ruiz, M. Cuéllar, M. Calvo-Flores, and M. Jiménez, “An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings,” Energies, vol. 9, no. 9, p. 684, Aug. 2016, doi: 10.3390/en9090684.
54. [54] F. Çoban and L. Demir, “Yapay Sinir Ağları ve Destek Vektör Regresyonu ile Talep Tahmini: Gıda İşletmesinde Bir Uygulama,” Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Derg., vol. 23, no. 67, pp. 327–338, Jan. 2021, doi: 10.21205/deufmd.2021236729.
55. [55] Ü. Ç. Büyükşahin and Ş. Ertekin, “Tek değişkenli zaman serileri tahmini için öznitelik tabanlı hibrit ARIMA-YSA modeli,” Gazi Üniversitesi Mühendislik Mimar. Fakültesi Derg., vol. 35, no. 1, pp. 467–478, 2020, doi: 10.17341/GAZIMMFD.508394.
56. [56] M. B. Erturan and F. Merdivenci, “Zaman serileri analizi için optimize ARIMA-YSA melez modeli,” Gazi Üniversitesi Mühendislik Mimar. Fakültesi Derg., vol. 37, no. 2, pp. 1019–1032, 2022, doi: 10.17341/GAZIMMFD.889513.
57. [57] L. Wang, H. Zou, J. Su, L. Li, and S. Chaudhry, “An ARIMA-ANN Hybrid Model for Time Series Forecasting,” Syst. Res. Behav. Sci., vol. 30, no. 3, pp. 244–259, May 2013, doi: 10.1002/SRES.2179.
58. [58] M. Khashei and M. Bijari, “A novel hybridization of artificial neural networks and ARIMA models for time series forecasting,” Appl. Soft Comput., vol. 11, no. 2, pp. 2664–2675, Mar. 2011, doi: 10.1016/J.ASOC.2010.10.015.
59. [59] C. N. Babu and B. E. Reddy, “A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data,” Appl. Soft Comput., vol. 23, pp. 27–38, Oct. 2014, doi: 10.1016/j.asoc.2014.05.028.
60. [60] Colin D. Lewis, “Industrial and Business Forecasting Methods: A Practical Guide to Exponential Smoothing and Curve Fitting,” Butterworth Sci., no. June 1981, pp. 111–153, 1982.