Application of meta-synthesis in identifying methods of data-based algorithms for troubleshooting the polypropylene production process

Subject Areas : Industrial Management

Soleiman Golpour Kandeh ¹ (PhD Candidate, Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
Reza Ramazani Khorshiddoost ² (Assistant Professor, Department of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran)
Mohammadreza kabarandadeh Ghadim ³ (Associate Professor, Department of Management, Center Tehran Branch, Islamic Azad University, Tehran, Iran)

Received: 2024-01-28 Accepted : 2024-04-25 Published : 2024-09-22

Keywords: Data-based algorithms, artificial intelligence, polypropylene production process troubleshooting, Meta-Synthesis.,

Abstract :

This study aimed to identify data-based algorithmic methods for troubleshooting the polypropylene production process. Employing a systematic review approach, we analyzed the findings of prior researchers and identified effective factors using the Seven-step method of Sandelowski and Barroso. Out of 500 articles, 34 were selected using the CASP method. To assess reliability and quality control, we utilized the Kappa index, which demonstrated excellent agreement for the identified indicators. Data analysis was conducted using ATLAS TI software, leading to the identification of 9 algorithms and their associated techniques, including ARIMA models, classification-based algorithms, decision trees, dynamic Bayesian networks, hybrid models, sample-based algorithms, hidden variable models, artificial intelligence networks, and rule-based models. Notably, 84% of the selected studies employed machine learning techniques falling into one of four categories: decision trees, artificial neural networks, hybrid models, and latent variable models. Furthermore, computational efficiency is recognized as a significant advantage for machine learning algorithms.

References:

Aldrini, J., Chihi, I., & Sidhom, L. (2023). Fault diagnosis and self-healing for smart manufacturing: a review. Journal of Intelligent Manufacturing, 1-33.
Armenise, S., Wong, S., Ramírez-Velásquez, J. M., Launay, F., Wuebben, D., Nyakuma, B. B., ... & Muñoz, M. (2021). Application of computational approach in plastic pyrolysis kinetic modelling: a review. Reaction kinetics, mechanisms and catalysis, 134(2), 591-614. https://doi.org/10.1007/s11144-021-02093-7
Bampoula, X., Siaterlis, G., Nikolakis, N., & Alexopoulos, K. (2021). A deep learning model for predictive maintenance in cyber-physical production systems using lstm autoencoders. Sensors, 21(3), 972.
Baptista, M., Sankararaman, S., de Medeiros, I. P., Nascimento Jr, C., Prendinger, H., & Henriques, E. M. (2018). Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling. Computers & Industrial Engineering, 115, 41-53.https://doi.org/10.1016/j.cie.2017.10.033
Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in industry, 101, 1-12.
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using big data frameworks. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 70–77). IEEE.
Carvalho, T. P., Soares, F. A. A. M. N., Vita, R., Francisco, R. P., Basto, J. P., & Alcalá, S. G. S. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024. https://doi.org/10.1016/j.cie.2019.106024
Chang, H., Zhang, G., Sun, Y., & Lu, S. (2022). Non-dominant genetic algorithm for multi-objective optimization design of unmanned aerial vehicle shell process. Polymers, 14(14), 2896. https://doi.org/10.3390/polym14142896
Chen, B., Liu, Y., Zhang, C., & Wang, Z. (2020). Time series data for equipment reliability analysis with deep learning. IEEE Access, 8, 105484–105493.
Fernandes, M., Canito, A., Corchado, J. M., & Marreiros, G. (2019, June). Fault detection mechanism of a predictive maintenance system based on autoregressive integrated moving average models. In International Symposium on Distributed Computing and Artificial Intelligence (pp. 171-180). Cham: Springer International Publishing.
Fernandes, M., Corchado, J. M., & Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: A systematic literature review. Applied Intelligence, 52, 14246–14280. https://doi.org/10.1007/s10489-022-03344-3
Gao, H., Zhang, Y., Zhou, X., et al. (2018). Intelligent methods for the process parameter determination of plastic injection molding. Frontiers of Mechanical Engineering, 13, 85–95. https://doi.org/10.1007/s11465-018-0491-0
Hu, H., Tang, B., Gong, X., Wei, W., & Wang, H. (2017). Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Transactions on Industrial Informatics, 13(4), 2106–2116. https://doi.org/10.1109/TII.2017.2683528
Hwang, I., Kim, S., Kim, Y., & Seah, C. E. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18(3), 636–653. https://doi.org/10.1109/TCST.2009.2026285
Ke, K. C., Wu, P. W., & Huang, M. S. (2023). Multi-quality prediction of injection molding parts using a hybrid machine learning model. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-023-12329-6
Kiangala, K. S., & Wang, Z. (2020). An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. IEEE Access, 1. https://doi.org/10.1109/ACCESS.2020.3006788
Kolokas, N., Vafeiadis, T., Ioannidis, D., & Tzovaras, D. (2018, July). Forecasting faults of industrial equipment using machine learning classifiers. In 2018 Innovations in Intelligent Systems and Applications (INISTA) (pp. 1-6). IEEE.
Lau, C. K., Heng, Y. S., Hussain, M. A., & Nor, M. M. (2010). Fault diagnosis of the polypropylene production process (UNIPOL PP) using ANFIS. ISA transactions, 49(4), 559-566.
Leiviskä, K. (2005). Problem Definition—From Applications to Methods. In Do Smart Adaptive Systems Exist? Best Practice for Selection and Combination of Intelligent Methods (pp. 19-26). Berlin, Heidelberg: Springer Berlin Heidelberg.
Liu, C., Bai, J., & Wu, F. (2022). Fault Diagnosis Using Dynamic Principal Component Analysis and GA Feature Selection Modeling for Industrial Processes. Processes, 10(12), 2570. https://doi.org/10.3390/pr10122570
Liu, C., Tang, D., Zhu, H., & Nie, Q. (2021). A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system. IEEE Access, 9, 49557–49575. https://doi.org/10.1109/ACCESS.2021.3069256
Mathew, V., Toby, T., Singh, V., Rao, B. M., & Kumar, M. G. (2017, December). Prediction of Remaining Useful Lifetime (RUL) of turbofan engine using machine learning. In 2017 IEEE international conference on circuits and systems (ICCS) (pp. 306-311). IEEE.
Mobley, R. K. (2001). Predictive Maintenance. In Plant Engineer’s Handbook (pp. 867–888). Elsevier.
Mohan, T. R., Roselyn, J. P., Uthra, R. A., Devaraj, D., & Umachandran, K. (2021). Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery. Computers & Industrial Engineering, 157, 107267. https://doi.org/10.1016/j.cie.2021.107267
Naskos, A., Gounaris, A., Metaxa, I., & Köchling, D. (2019). Detecting anomalous behavior towards predictive maintenance. In Advanced Information Systems Engineering Workshops: CAiSE 2019 International Workshops, Rome, Italy, June 3-7, 2019, Proceedings 31 (pp. 73-82). Springer International Publishing.
Nieves Avendano, D., Caljouw, D., Deschrijver, D., & Van Hoecke, S. (2021). Anomaly detection and event mining in cold forming manufacturing processes. The International Journal of Advanced Manufacturing Technology, 115, 837-852.
Niknam, S. A., Kobza, J., & Hines, J. W. (2017). Techniques of trend analysis in degradation-based prognostics. International Journal of Advanced Manufacturing Technology, 88, 2429–2441. https://doi.org/10.1007/s00170-016-8909-5
O’Donovan, C., Popov, I., Todeschini, G., et al. (2023). Ladle pouring process parameter and quality estimation using Mask R-CNN and contrast-limited adaptive histogram equalisation. International Journal of Advanced Manufacturing Technology, 126, 1397–1416. https://doi.org/10.1007/s00170-023-11151-4
O’donovan, P., Leahy, K., Bruton, K., & O’Sullivan, D. T. (2015). Big data in manufacturing: a systematic mapping study. Journal of Big Data, 2, 1-22. https://doi.org/10.1186/s40537-015-0028-x
Peng, D., Xu, Y., & Zhu, Q. (2013). Study and Application of Case-based Extension Fault Diagnosis for Chemical Process. Chinese Journal of Chemical Engineering, 21(4), 366–375. doi:10.1016/s1004-9541(13)60484-x
Ribeiro Junior, R. F., & Gomes, G. F. (2023). On the Use of Machine Learning for Damage Assessment in Composite Structures: A Review. Applied Composite Materials, 1-37.
Shen, Y. (2023). Genetic algorithm based production knowledge base for mechanical fault detection model. Journal of Computational Methods in Sciences and Engineering, 23.
Srinivasan, R. (2007). Artificial intelligence methodologies for agile refining: An overview. Knowledge and Information Systems, 12, 129–145. https://doi.org/10.1007/s10115-006-0057-z
Sullivan, G., Pugh, R., Melendez, A. P., & Hunt, W. D. (2010). Operations & maintenance best practices-a guide to achieving operational efficiency (release 3). Technical Report, Pacific Northwest National Lab. (PNNL), Richland.
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S., & Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3), 812–820. https://doi.org/10.1109/TII.2014.2349359
Tayyebi, S. (2015). Multiple-Fault Diagnosing System Based on Fuzzy-Genetic Algorithm for the Production of Toluene from n-Heptane. Farayandno, 9(48), 44-52.
Turkoglu, B., Komesli, M., & Unluturk, M. S. (2019). Application of data mining in failure estimation of cold forging machines: An industrial research. Stud Inf Control, 28(1), 87-94. https://doi.org/10.24846/v28i1y201909
Uhlmann, E., Pontes, R. P., Geisert, C., & Hohwieler, E. (2018). Cluster identification of sensor data for predictive maintenance in a selective laser melting machine tool. Procedia Manuf, 24, 60-65. https://doi.org/10.1016/j.promfg.2018.06.009
Vrabic, R., Kozjek, D., & Butala, P. (2017). Knowledge elicitation for fault diagnostics in plastic injection moulding: A case for machine-to-machine communication. CIRP Ann Manuf Technol, 66(1), 433-436.
Xiao, H., Xu, Z. Z., Kim, L. S., et al. (2015). Optimization scheme of genetic algorithm and its application on aeroengine fault diagnosis. Int J Precis Eng Manuf, 16, 735-741. https://doi.org/10.1007/s12541-015-0097-y
Xie, C., Zhang, R., & Bhola, J. (2022). Research on fault detection and identification methods of nonlinear dynamic process based on ICA. Nonlinear Engineering, 11(1), 13-19. https://doi.org/10.1515/nleng-2022-0003
Yu, J. (2018). Tool condition prognostics using logistic regression with penalization and manifold regularization. Appl Soft Comput, 64, 454-467. https://doi.org/10.1016/j.asoc.2017.12.042
Zhang, J., Roberts, P. D., & Ellis, J. E. (1990). Fault diagnosis of a mixing process using deep qualitative knowledge representation of physical behaviour. J Intell Robot Syst, 3, 103-115. https://doi.org/10.1007/BF00242159
Zhang, W., Yang, D., & Wang, H. (2019). Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst J, 13(3), 2213-2227. https://doi.org/10.1109/JSYST.2019.2905565
Zhao, N. Y., Lian, J. Y., Wang, P. F., et al. (2020). Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: A review. Int J Adv Manuf Technol, 120, 85-101. https://doi.org/10.1007/s00170-022-08859-0
Zhou, Y., Fang, X., & Zhao, B. (2001, June). Diagnosis based on genetic algorithms and fuzzy logic in NPPs. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 359-366). Berlin, Heidelberg: Springer Berlin Heidelberg.