Determining and forecasting oee based on reliability and maintainability using polynomial regression and neural networks

محورهای موضوعی : Maintenance management

Ratna Mayasari ¹ , Robby Marlon Brando ² , Agus Widodo ³ , Ardani Cesario Zuhri ⁴ , Nasril Nasril ⁵ , Mario Ardhany ⁶ , Arwidya Tantri Agtusia ⁷ , Ellia Kristiningrum ⁸

1 - National Research and Innovation Agency
2 - National Research and Innovation Agency
3 - National Research and Innovation Agency
4 - National Research and Innovation Agency
5 - National Research and Innovation Agency
6 - National Research and Innovation Agency
7 - National Research and Innovation Agency
8 - National Research and Innovation Agency

تاریخ دریافت : 1403/06/20 تاریخ پذیرش : 1403/07/30 تاریخ انتشار : 1403/08/01

کلید واژه: oee, mtbf, mttr, regression, neural network,

چکیده مقاله :

Production maintenance ensures equipment remains in good working order, enabling the generation of goods complying with specifications. The purpose of this research was to assess and evaluate the machine performance in one of the plastic industries. This research will determine how machine performance is correlated with OEE, MTBF, and MTTR, optimise the OEE variables and forecast the future OEE values. The relationship between OEE, MTBF, and MTTR has been analysed by linear and non-linear regression using polynomial and artificial neural networks (ANN). Meanwhile, the OEE optimization is performed using SciPy optimizer on linear and nonlinear objective functions, whereas the OEE forecasting employs Convolutional Neural Network (CNN) in addition to the ANN and the polynomials. All regression analysis indicate OEE is well explained by MTBF and MTTR as all R-squared values are above 95%. Specifically, those R-squared values are 98.25%, 97.78%, 97.64%, and 95.56%, for ANN, polynomial degree 3, degree 2 and degree 1, respectively. Furthermore, the optimal value of MTBF is found to be at least 3.706 whereas that of MTTR is at most 0.899 hours to achieve an OEE value of at least 0.85. Lastly, the accuracy of OEE predictions using CNN achieves the best performance by having the lowest RMSE of 0.0156, followed by ANN with an RMSE of 0.0166, and the polynomials with RMSEs of around 0.02.

چکیده انگلیسی:

Production maintenance ensures equipment remains in good working order, enabling the generation of goods complying with specifications. The purpose of this research was to assess and evaluate the machine performance in one of the plastic industries. This research will determine how machine performance is correlated with OEE, MTBF, and MTTR, optimise the OEE variables and forecast the future OEE values. The relationship between OEE, MTBF, and MTTR has been analysed by linear and non-linear regression using polynomial and artificial neural networks (ANN). Meanwhile, the OEE optimization is performed using SciPy optimizer on linear and nonlinear objective functions, whereas the OEE forecasting employs Convolutional Neural Network (CNN) in addition to the ANN and the polynomials. All regression analysis indicate OEE is well explained by MTBF and MTTR as all R-squared values are above 95%. Specifically, those R-squared values are 98.25%, 97.78%, 97.64%, and 95.56%, for ANN, polynomial degree 3, degree 2 and degree 1, respectively. Furthermore, the optimal value of MTBF is found to be at least 3.706 whereas that of MTTR is at most 0.899 hours to achieve an OEE value of at least 0.85. Lastly, the accuracy of OEE predictions using CNN achieves the best performance by having the lowest RMSE of 0.0156, followed by ANN with an RMSE of 0.0166, and the polynomials with RMSEs of around 0.02.

منابع و مأخذ:

Agarap, A. F. M. (2018). Deep Learning using Rectified Linear Units (ReLU). 1803.08375v2 [cs.NE] 7 Feb 2019.

Ahmadi, S., Moosazadeh, S., Hajihassani, M., Moomivand, H., & Rajaei, M. M. (2019a). Reliability, availability and maintainability analysis of the conveyor system in mechanized tunneling. Measurement: Journal of the International Measurement Confederation, 145, 756–764. https://doi.org/10.1016/j.measurement.2019.06.009

Ahmadi, S., Moosazadeh, S., Hajihassani, M., Moomivand, H., & Rajaei, M. M. (2019b). Reliability, availability and maintainability analysis of the conveyor system in mechanized tunneling. Measurement, 145, 756–764. https://doi.org/10.1016/j.measurement.2019.06.009

Alavian, P., Eun, Y., Liu, K., Meerkov, S. M., & Zhang, L. (2018). The (α, β)-Precise Estimates of MTBF and MTTR: Definitions, Calculations, and Effect on Machine Efficiency and Throughput Evaluation in Serial Production Lines.

Al-Toubi, S. (2023). Evaluating and Predicting Overall Equipment Effectiveness for Deep Water Disposal Pump using ANN- GA Analysis Approach. Journal of Mechanical Engineering, 20(2), 199–225. https://doi.org/10.24191/jmeche.v20i2.22063

Asesh, A., & Dugar, M. (2023). Time Series Prediction using Convolutional Neural Networks. 2023 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6. https://doi.org/10.1109/ICMLANT59547.2023.10372968

Boldyrieva, L., Zelinska, H., Krapkina, V., & Komelina, A. (2019). Problems and Solutions of Transport Logistics. Proceedings of the 2019 7th International Conference on Modeling, Development and Strategic Management of Economic System (MDSMES 2019). Proceedings of the 2019 7th International Conference on Modeling, Development and Strategic Management of Economic System (MDSMES 2019), Ivano-Frankivsk, Ukraine.
https://doi.org/10.2991/mdsmes-19.2019.59

Chandra, R., Goyal, S., & Gupta, R. (2021). Evaluation of deep learning models for multi-step ahead time series prediction. IEEE Access, 9, 83105–83123. https://doi.org/10.1109/ACCESS.2021.3085085

Crone, S. F., & Kourentzes, N. (2009). Forecasting Seasonal Time Series with Multilayer Perceptrons – an empirical Evaluation of Input Vector Specifications for deterministic Seasonality.

Csáji, B. C. (2001). Approximation with Artificial Neural Networks. Faculty of Mathematics and Computing Science.
Daniel Pen ̃ a, & Ismael Sa ́ nchez. (2006). MEASURING THE ADVANTAGES OF MULTIVARIATE VS. UNIVARIATE FORECASTS (arXiv:1912.07383). arXiv. https://doi.org/10.1111/j.1467-9892.2007.00538.x

Dervitsiotis, K. N. (1981). Operations management. New York : McGraw-Hill.
El Mazgualdi, C., Masrour, T., El Hassani, I., & Khdoudi, A. (2020). Using Machine Learning for Predicting Efficiency in Manufacturing Industry. In M. Ezziyyani (Ed.), Advanced Intelligent Systems for Sustainable Development (AI2SD’2019) (Vol. 1104, pp. 750–762). Springer International Publishing. https://doi.org/10.1007/978-3-030-36671-1_68

García, F. J. Á., & Salgado, D. R. (2022). Analysis of the Influence of Component Type and Operating Condition on the Selection of Preventive Maintenance Strategy in Multistage Industrial Machines: A Case Study. Machines, 10(5), 385. https://doi.org/10.3390/machines10050385

Garza-Reyes, J. A. (2015). From measuring overall equipment effectiveness (OEE) to overall resource effectiveness (ORE). Journal of Quality in Maintenance Engineering, 21(4), 506–527. https://doi.org/10.1108/JQME-03-2014-0014

Haber, N., & Fargnoli, M. (2022). Product-Service Systems for Circular Supply Chain Management: A Functional Approach. Sustainability, 14(22), 14953. https://doi.org/10.3390/su142214953

Hosseini, S. M., Shahanaghi, K., & Shasfand, S. (2024). Functional Model of Integrated Maintenance (Reliability, Overall Equipment Effectiveness, Safety, and Cost) in Petrochemical Industries. International Journal of Engineering, 37(11), 2392–2404. https://doi.org/10.5829/IJE.2024.37.11B.23

Hou, J., Adhikari, B., & Cheng, J. (2018). DeepSF: Deep convolutional neural network for mapping protein sequences to folds. Bioinformatics, 34(8), 1295–1303. https://doi.org/10.1093/bioinformatics/btx780

Hsu, D., Sanford, C., Servedio, R. A., & Gkaragkounis, E. V. V. (2021). On the Approximation Power of Two-Layer Networks of Random ReLUs.

Islam, M. S. (2012). Prospects And Challenges Of Plastic Industries In Bangladesh. Journal of Chemical Engineering, 26, 16–21. https://doi.org/10.3329/jce.v26i1.10176

Jacob Hallman. (2019). 39. Acomparative study on Linear Rergression.pdf. Stockholm, Sweden.

Jahn, M. (2018). Artificial neural network regression models: Predicting GDP growth. Hamburg Institute of International Economics (HWWI), HWWI Research Paper, No. 185. https://hdl.handle.net/10419/182108

Jason Brownlee. (2020, August 28). How to Develop Convolutional Neural Network Models for Time Series Forecasting. https://machinelearningmastery.com/how-to-develop-convolutional-neural-network-models-for-time-series-forecasting/

Jia, Y., Wu, F., Liu, P., Zhou, G., Yu, B., Lou, X., & Xia, F. (2019). A label-free fluorescent aptasensor for the detection of Aflatoxin B1 in food samples using AIEgens and graphene oxide. Talanta, 198, 71–77. https://doi.org/10.1016/j.talanta.2019.01.078

Kechaou, F., Addouche, S.-A., & Zolghadri, M. (2024). A comparative study of overall equipment effectiveness measurement systems. Production Planning & Control, 35(1), 1–20. https://doi.org/10.1080/09537287.2022.2037166

Kifta, D. A., & Putri, N. T. (2021). Analysis and Measurement of Overall Equipment Effectiveness (OEE) Values of the CNC Cutting Machine at PT. XYZ. 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 953–958. https://doi.org/10.1109/IEEM50564.2021.9672603

Kumari, K., & Yadav, S. (2018). Linear regression analysis study. Journal of the Practice of Cardiovascular Sciences, 4(1), 33. https://doi.org/10.4103/jpcs.jpcs_8_18

Larky, M., & Javidrad, H. (2019). Utilization of New Definitions to Calculate Overall Equipment Effectiveness (OEE) for Air Compressors:A Case Study. International Journal of Science and Engineering Applications, 8(9), 423–426. https://doi.org/10.7753/IJSEA0809.1004
Lepot, M., Aubin, J.-B., & Clemens, F. (2017). Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment. Water, 9(10), 796. https://doi.org/10.3390/w9100796
Li, G., Li, Y., Zhang, X., Hou, C., He, J., Xu, B., & Chen, J. (2018). Development of a Preventive Maintenance Strategy for an Automatic Production Line Based on Group Maintenance Method. Applied Sciences, 8(10), 1781. https://doi.org/10.3390/app8101781
Marcellino, M., Stock, J. H., & Watson, M. W. (2006). A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series. Journal of Econometrics, 135(1–2), 499–526. https://doi.org/10.1016/j.jeconom.2005.07.020
Markova, M. (2022). Convolutional neural networks for forex time series forecasting. 030024. https://doi.org/10.1063/5.0083533

Maulud, D., & Abdulazeez, A. M. (2020). A Review on Linear Regression Comprehensive in Machine Learning. Journal of Applied Science and Technology Trends, 1(2), 140–147. https://doi.org/10.38094/jastt1457

Michlowicz, E. (2019). THE IMPACT OF SHUTDOWNS AND EQUIPMENT FAILURES ON THE EFFICIENCY OF THE ELECTROTECHNICAL SHEET PRODUCTION LINE. 1960–1966. https://doi.org/10.37904/metal.2019.972

Musial, J. P., Verstraete, M. M., & Gobron, N. (2011). Technical Note: Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series. Atmospheric Chemistry and Physics, 11(15), 7905–7923. https://doi.org/10.5194/acp-11-7905-2011


Mwanza, B. G., & Mbohwa, C. (2017). Major Obstacles to Sustainability in the Plastic Industry. Procedia Manufacturing, 8, 121–128. https://doi.org/10.1016/j.promfg.2017.02.021

Nagi, J., Giusti, A., Nagi, F., Gambardella, L. M., & Di Caro, G. A. (2014). Online feature extraction for the incremental learning of gestures in human-swarm interaction. 2014 IEEE International Conference on Robotics and Automation (ICRA), 3331–3338. https://doi.org/10.1109/ICRA.2014.6907338

Nakajima, S. (1988). Introduction to TPM: Total Productive Maintenance. Preventative Maintenance Series. Productivity Press.

Nayak, D. M. (2013). EVALUATION OF OEE IN A CONTINUOUS PROCESS INDUSTRY ON AN INSULATION LINE IN A CABLE MANUFACTURING UNIT. 2(5).

Nayak, S., Dhua, U., & Samanta, S. (2020). Antagonistic activity of cowshed bacillus sp. Bacteria against aflatoxigenic and sclerotic aspergillus flavus. Journal of Biological Control, 34(1), 52–58. https://doi.org/10.18311/jbc/2020/24839

Nurcahyo, R., Darmawan, D., Jannis, Y., Kurniati, A., & Habiburrahman, M. (2019). Maintenance Planning Key Process Area: Case Study at Oil Gas Industry in Indonesia. IEEE International Conference on Industrial Engineering and Engineering Management, 2019-Decem(December), 1704–1708. https://doi.org/10.1109/IEEM.2018.8607527

Nurcahyo, R., Tri Nugroho, F. W., & Kristiningrum, E. (2023). RELIABILITY, AVAILABILITY, AND MAINTAINABILITY (RAM) ANALYSIS FOR PERFORMANCE EVALUATION OF POWER GENERATION MACHINES. Jurnal Standardisasi, 25(1), 41. https://doi.org/10.31153/js.v25i1.982

Nurcahyo, R., Winanda, L. D., & Isharyadi, F. (2023). ANALISIS KUALITAS KINERJA MESIN WRAPPING PADA INDUSTRI PANGAN DENGAN METODE OVERALL EQUIPMENT EFFECTIVENESS (OEE): STUDI KASUS DI INDUSTRI MAKANAN RINGAN. Jurnal Standardisasi, 25(1), 1. https://doi.org/10.31153/js.v25i1.988

Ouhader, H., & Kyal, M. E. (2020). Assessing the economic and environmental benefits of horizontal cooperation in delivery: Performance and scenario analysis. Uncertain Supply Chain Management, 303–320. https://doi.org/10.5267/j.uscm.2019.12.001

Pamungkas, I., Irawan, H. T., & Pandria, T. M. A. (2021). IMPLEMENTASI PREVENTIVE MAINTENANCE UNTUK MENINGKATKAN KEANDALAN PADA KOMPONEN KRITIS BOILER DI PEMBANGKIT LISTRIK TENAGA UAP. VOCATECH: Vocational Education and Technology Journal, 2(2), 73–78. https://doi.org/10.38038/vocatech.v2i2.53

Pathak, P., Sharma, S., & Ramakrishna, S. (2023). Circular transformation in plastic management lessens the carbon footprint of the plastic industry. Materials Today Sustainability, 22, 100365. https://doi.org/10.1016/j.mtsust.2023.100365

Patil, S., & Patil, S. (2021). Linear with polynomial regression: Overview. International Journal of Applied Research, 7(8), 273–275. https://doi.org/10.22271/allresearch.2021.v7.i8d.8876

Pérez-Enciso & Zingaretti. (2019). A Guide for Using Deep Learning for Complex Trait Genomic Prediction. Genes, 10(7), 553. https://doi.org/10.3390/genes10070553

Ribeiro, I. M., Godina, R., Pimentel, C., Silva, F. J. G., & Matias, J. C. O. (2019). Implementing TPM supported by 5S to improve the availability of an automotive production line. Procedia Manufacturing, 38, 1574–1581. https://doi.org/10.1016/j.promfg.2020.01.128

Sabyasachi Sahoo. (2018, August 19). Deciding optimal kernel size for CNN. https://towardsdatascience.com/deciding-optimal-filter-size-for-cnns-d6f7b56f9363

Sayuti et al. (2019). Analysis of the Overall Equipment Effectiveness (OEE) to Minimize Six Big Losses of Pulp Machine: A Case Study in Pulp and Paper Industries. IOP Conference Series: Materials Science and Engineering, 536(1), 011001. https://doi.org/10.1088/1757-899X/536/1/011001

Simon, F., Javad, B., & Abbas, B. (2014). Availability analysis of the main conveyor in the Svea Coal Mine in Norway. International Journal of Mining Science and Technology, 24(5), 587–591. https://doi.org/10.1016/j.ijmst.2014.07.004

Stenström, C., Norrbin, P., Parida, A., & Kumar, U. (2016). Preventive and corrective maintenance – cost comparison and cost–benefit analysis. Structure and Infrastructure Engineering, 12(5), 603–617. https://doi.org/10.1080/15732479.2015.1032983

Taieb, S. B., & Hyndman, R. J. (2012). Recursive and direct multi-step forecasting: The best of both worlds.
Thomas, A. J., Petridis, M., Walters, S. D., Gheytassi, S. M., & Morgan, R. E. (2015). On Predicting the Optimal Number of Hidden Nodes. 2015 International Conference on Computational Science and Computational Intelligence (CSCI), 565–570. https://doi.org/10.1109/CSCI.2015.33

Uyanık, G. K., & Güler, N. (2013). A Study on Multiple Linear Regression Analysis. Procedia - Social and Behavioral Sciences, 106, 234–240. https://doi.org/10.1016/j.sbspro.2013.12.027

Wang, et al. (2023). 38. TimeSeries Well Performance Prediction Based Convolutional and Long Short‑Term Memory Neural.pdf.

Wang, K. (2013). A STUDY OF CUBIC SPLINE INTERPOLATION. RIVIER ACADEMIC JOURNAL, VOL 9 NUMBER 2, FALL 2013.

William W. S. Wei. (2006). Time Series Analysis: Univariate and Multivariate Methods. Perarson Education. Inc.