A Data-Driven Methodology to Assess Raw Materials Impact on Manufacturing Systems Breakdowns
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Published
Apr 17, 2024
Maha Ben Ayed
Moncef Soualhi
Raouf Ketata
Nicolas Mairot
Sylvian Giampiccolo
Noureddine Zerhouni
Abstract
Data-driven Prognostics and Health Management (PHM) become a crucial layer in the realm of predictive maintenance (PM). However, many industries develop PM technologies based on the monitoring of machine data to anticipate failures without considering the injected raw material. In reality, non-compliant material characteristics can affect the manufacturing tools leading to machine breakdowns and poor quality product. To cope with this situation, this paper proposes a new methodology that helps operators predicting machine breakdowns. In detail, the methodology starts by implementing an Extract, Transform, Load (ETL) process which aims to create a new and reliable dataset from heterogeneous sources. Then, a feature selection method is used for dimensionality reduction and keep only useful information. After that, the selected features are injected to Machine Learning (ML) algorithms to predict system breakdown occurrences. Finally, the novelty in this study, an auto-labeling algorithm based on material data and machine breakdown predictions is proposed. This algorithm aims to enhance raw material stock management, scheduling their consumption accordingly and thus reducing machine breakdowns. The developed methodology is applied to a real dataset of a French company, SCODER, that shows and pointed out promising perspectives in PM.
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Keywords
Prognostics and Health Management, Manufacturing, Raw Material Data, Extract-Transform-Load, Features Selection, Machine Learning, Auto-labeling
References
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Achouch, M., Dimitrova, M., Ziane, K., Sattarpanah Karganroudi, S., Dhouib, R., Ibrahim, H., & Adda, M. (2022). On predictive maintenance in industry 4.0: Overview, models, and challenges. Applied Sciences, 12(16), 8081.
Arshadi, M., Gref, R., Geladi, P., Dahlqvist, S.-A., & Lestander, T. (2008). The influence of raw material characteristics on the industrial pelletizing process and pellet quality. Fuel processing technology, 89(12), 1442–1447.
Borràs-Ferrís, J., Palací-López, D., Duchesne, C., & Ferrer, A. (2022). Defining multivariate raw material specifications in industry 4.0. Chemometrics and Intelligent Laboratory Systems, 225, 104563.
Farahat, A., Gupta, C., et al. (2020). Similarity-based feature extraction from vibration data for prognostics. In Annual conference of the phm society (Vol. 12, pp. 10–10).
Gelzinis, A., Verikas, A., Vaiciukynas, E., Bacauskiene, M., Minelga, J., Hallander, M., . . . Padervinskis, E. (2014). Exploring sustained phonation recorded with acoustic and contact microphones to screen for laryngeal disorders. In 2014 ieee symposium on computational intelligence in healthcare and e-health (cicare) (pp. 125–132).
Li, C., Sanchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D.,& Vásquez, R. E. (2016). Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals. Mechanical Systems and Signal Processing, 76, 283–293.
Li, Y., Zhang, Q., Zhu, Y., Yang, A., Liu, W., Zhao, X., . . . others (2022). A model study on raw material chemical composition to predict sinter quality based on ga-rnn. Computational Intelligence and Neuroscience, 2022.
Mera-Gaona, M., L´opez, D. M., Vargas-Canas, R., & Neumann, U. (2021). Framework for the ensemble of feature selection methods. Applied Sciences, 11(17), 8122.
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022). Explainable artificial intelligence: a comprehensive review. Artificial Intelligence Review, 1–66.
Montesinos López, O. A., Montesinos López, A., & Crossa, J. (2022). Overfitting, model tuning, and evaluation of prediction performance. In Multivariate statistical machine learning methods for genomic prediction (pp. 109–139). Springer.
Popescu, T. D., & Aiordachioaie, D. (2019). Fault detection of rolling element bearings using optimal segmentation of vibrating signals. Mechanical Systems and Signal Processing, 116, 370–391.
Rahman, M. M., Usman, O. L., Muniyandi, R. C., Sahran,S., Mohamed, S., & Razak, R. A. (2020). A review of machine learning methods of feature selection and classification for autism spectrum disorder. Brain sciences, 10(12), 949.
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017). Machine learning in materials informatics: recent applications and prospects. npj Computational Materials, 3(1), 54.
Raouf, I., Khan, A., Khalid, S., Sohail, M., Azad, M. M., & Kim, H. S. (2022). Sensor-based prognostic health management of advanced driver assistance system for autonomous vehicles: A recent survey. Mathematics, 10(18), 3233.
Rizzo, A., Goel, S., Luisa Grilli, M., Iglesias, R., Jaworska, L., Lapkovskis, V., . . . Valerini, D. (2020). The critical raw materials in cutting tools for machining applications: A review. Materials, 13(6), 1377.
Soualhi, M., Nguyen, K. T., Soualhi, A., Medjaher, K., & Hemsas, K. E. (2019). Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals. Measurement, 141, 37–51.
Soualhi, M., Soualhi, A., Nguyen, K. T., Medjaher, K., Clerc, G., & Razik, H. (2023). Open heterogeneous data for condition monitoring of multi faults in rotating machines used in different operating conditions. International Journal of Prognostics and Health Management, 14(2).
Stauffer, F., Vanhoorne, V., Pilcer, G., Chavez, P.-F., Vervaet, C., & De Beer, T. (2019). Managing api raw material variability in a continuous manufacturing line–prediction of process robustness. International Journal of Pharmaceutics, 569, 118525.
Thirumagal, R., Suganthy, R., Mahima, S., & Kesavaraj, G. (2014). Etl tools in data mining—a review. International Journal of Research in Computer Applications & Robotics, 2(1), 62–69.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126, 3–13.
Winursito, A., Hidayat, R., Bejo, A., & Utomo, M. N. Y. (2018). Feature data reduction of mfcc using pca and svd in speech recognition system. In 2018 international conference on smart computing and electronic enterprise (icscee) (pp. 1–6).
Zhang, Y., Sheng, M., Liu, X., Wang, R., Lin, W., Ren, P., . . . Song, W. (2022). A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration. Health Information Science and Systems, 10(1), 22.
Zonta, T., da Costa, C. A., Zeiser, F. A., de Oliveira Ramos, G., Kunst, R., & da Rosa Righi, R. (2022). A predictive maintenance model for optimizing production schedule using deep neural networks. Journal of Manufacturing Systems, 62, 450–462
Section
Technical Papers