Machine Learning–Driven Predictive Maintenance for Smart Beverage Manufacturing Systems

Peerawit Pongtananikorn; Kunlapat Thongkaew

doi:10.55003/ETH.430305

Authors

Peerawit Pongtananikorn Faculty of Engineering, Prince of Songkla University, Thailand
Kunlapat Thongkaew Faculty of Engineering, Prince of Songkla University, Thailand

DOI:

https://doi.org/10.55003/ETH.430305

Keywords:

Maintenance, Predictive Maintenance (PdM), Beverage Manufacturing Process, Machine Learning

Abstract

This study proposed a machine learning-based predictive maintenance system in a beverage manufacturing process to enhance failure prediction accuracy and support improved maintenance planning. The approach focused on the beverage filling line, captured short-term temporal process dynamics using rolling-window feature extraction, and addressed the strong class imbalance between normal operation and breakdown events. Significant sensor variables were initially identified through correlation analysis, and the selected sensors and breakdown criteria were subsequently analyzed using various classification models. A 60-minute decision horizon was defined as a classification-based early-warning window derived from the time-to-failure (TTF) variable. Observations were labeled breakdown-proximate state when the next breakdown occurred within 60 minutes (TTF < 60 min) and as normal operation when the next breakdown occurred after 60 minutes (TTF > 60 min). Therefore, the model classifies a breakdown-proximate state rather than predicting the exact remaining time to failure. Model performance was evaluated using precision, recall, F1-score, ROC-AUC, and chronological walk-forward validation. Based on correlation analysis and process relevance, 15 sensors were selected as key features associated with process breakdown. The Random Forest model was the best performer, achieving an accuracy of 0.990, precision of 0.918, recall of 0.722, F1-score of 0.808, and ROC-AUC of 0.995 at the optimized threshold of 0.45. The recall result indicates acceptable detection of breakdown-proximate states, although 27.8% was missed as a false negative. Considering the balance between recall and precision, the precision exceeded 90%, showing that false alarms were maintained at a low level. These results demonstrate a practical trade-off between reducing missed breakdown warnings and avoiding excessive unnecessary inspections. Thus, the Random Forest is suitable for predicting breakdowns with accuracy, stability and robustness. Future work will extend the predictive maintenance results linked with failure-risk predictions to reliability metrics of time between failures (TBF) and mean time between failures (MTBF), improved alignment between maintenance actions and actual machine conditions.

References

J. Moubray, “Proactive Maintenance 1: Preventive Tasks,” in Reliability-Centered Maintenance, 2nd ed. New York, NY, USA: Industrial Press Inc., 1997, ch. 6, secs. 6.1–6.6, pp. 129–143.

J. Moubray, “Proactive Maintenance 2: Predictive Tasks,” in Reliability-Centered Maintenance, 2nd ed. New York, NY, USA: Industrial Press Inc., 1997, ch. 7, secs. 7.1–7.9, pp. 144–169.

R. K. Mobley, “Impact of Maintenance,” in An Introduction to Predictive Maintenance, 2nd ed., Boston, MA, USA: Butterworth-Heinemann, 2002, ch. 1, sec. 1.1, pp. 2–9.

A. K. S Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance,” Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483–1510, 2006, doi: 10.1016/j.ymssp.2005.09.012.

A. Heng, S. Zhang, A. C. C. Tan, and J. Mathew, “Rotating machinery prognostics: State of the art, challenges and opportunities,” Mechanical Systems and Signal Processing, vol. 23, no. 3, pp. 724–739, 2009, doi: 10.1016/j.ymssp.2008.06.009.

J. Lee, B. Bagheri, and H. -A. Kao, “A Cyber–Physical Systems architecture for Industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015, doi: 10.1016/j.mfglet.2014.12.001.

M. Compare, P. Baraldi, and E. Zio, “Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4585–4597, 2019, doi: 10.1109/JIOT.2019.2957029.

A. Bousdekis, D. Apostolou, and G. Mentzas, “Predictive Maintenance in the 4th Industrial Revolution: Benefits, Business Opportunities, and Managerial Implications,” IEEE Engineering Management Review, vol. 48, no. 1, pp. 57–62, 2019, doi: 10.1109/EMR.2019.2958037.

B. Hrnjica, and S. Softic, “Explainable AI in Manufacturing: A Predictive Maintenance Case Study,” in IFIP WG 5.7 International Conference, Novi Sad, Serbia, Aug. 30–Sep. 3, 2020, pp. 66–73, 2020.

T. Zonta, C. A. da Costa, R. da Rosa Righi, M. J. Lima, E. S. da. Trindade, and G. P. Li, “Predictive maintenance in the Industry 4.0: A systematic literature review,” Computers & industrial engineering, vol. 150, 2020, Art. no. 106889, doi: 10.1016/j.cie.2020.106889.

S. Ayvaz and K. Alpay, “Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real time,” Expert Systems with Applications, vol. 173, 2021, Art. no. 114598, doi: 10.1016/j.eswa.2021.114598.

Y. S. Cheong, C. S. Seah, Y. X. Loh, and L. H. Loh, “Artificial Intelligence (Ai) In The Food And Beverage Industry: Improves The Customer Experience,” in 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS), Ipoh, Malaysia, Sep. 8–9, 2021, pp. 1–6, doi: 10.1109/AiDAS53897.2021.9574261.

I. Ahmed, G. Jeon, and F. Piccialli, “From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where,” IEEE Transactions on Industrial Informatics, vol. 18, no. 8, pp. 5031–5042, 2022, doi: 10.1109/TII.2022.3146552.

V. Terziyan, and O. Vitko, “Explainable AI for Industry 4.0: Semantic Representation of Deep Learning Models,” Procedia Computer Science, vol. 200, pp. 216–226, 2022, doi: 10.1016/j.procs.2022.01.220.

Z. A. B. Sinnah, “Explainable AI-driven predictive maintenance for mitigating process safety risks in safety-critical industrial equipment,” Journal of Loss Prevention in the Process Industries, vol. 100, 2025, Art. no. 105907, doi: 10.1016/j.jlp.2025.105907.

C. Walker, V. Agarwal, L. Lin, A. Hall, R. Hill, T. Mortenson, and N. Lybeck, “Explainable Artificial Intelligence Technology for Predictive Maintenance,” Idaho National Laboratory, Idaho Falls, ID, USA, Rep. INL/RPT-23-74159, 2023.

A. Ucar, M. Karakose, and N. Kırımça, “Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends,” Applied Sciences, vol. 14, no. 2, 2024, Art. no. 898, doi:10.3390/app14020898.

T. Akyaz and D. Engın, “Machine Learning-Based Predictive Maintenance System for Artificial Yarn Machines,” IEEE Access, vol. 12, pp. 125446–125461, 2024, doi: 10.1109/ACCESS.2024.3454548.

Y. Lei, N. Li, L. Guo, N. Li, T. Yan, and J. Lin, “Machinery health prognostics: A systematic review from data acquisition to RUL prediction,” Mechanical Systems and Signal Processing, vol. 104, pp. 799–834, 2018, doi: 10.1016/j.ymssp.2017.11.016.

W. Zhang, D. Yang, and H. Wang, “Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey,” IEEE Systems Journal, vol. 13, no. 3, pp. 2213–2227, 2019, doi: 10.1109/JSYST.2019.2905565.

T. P. Carvalho, F. A. A. M. N. Soares, R. Vita, R. da P. Francisco, J. P. Basto, and S. G. S. Alcalá, “A systematic literature review of machine learning methods applied to predictive maintenance,” Computers & Industrial Engineering, vol. 137, 2019, Art. no. 106024, doi: 10.1016/j.cie.2019.106024.

P. Nunes, J. Santos, and E. Rocha, “Challenges in Predictive Maintenance – A Review,” CIRP Journal of Manufacturing Science and Technology, vol. 40, pp. 53–67, 2023, doi: 10.1016/j.cirpj.2022.11.004.

H. M. H. A. Alshkeili, S. J. Almheiri, and M. A. Khan, “Privacy-Preserving Interpretability: An Explainable Federated Learning Model for Predictive Maintenance in Sustainable Manufacturing and Industry 4.0,” AI, vol. 6, No. 6, 2025, Art. no. 117, doi: 10.3390/ai6060117.

M. Achouch, M. Dimitrova, R. Dhouib, H. Ibrahim, M. Adda, S. S. Karganroudi, K. Ziane, and A. Aminzadeh, “Predictive Maintenance and Fault Monitoring Enabled by Machine Learning: Experimental Analysis of a TA-48 Multistage Centrifugal Plant Compressor,” Applied Sciences, vol. 13, no. 3, 2023, Art. no. 1790, doi: 10.3390/app13031790.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. VanderPlas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

F. E. Bezerra, G. C. de Oliveira Neto, G. M. Cervi, R. F. Mazetto, A. M. de Faria, M. Vidoa, G. A. Lima, S. A. de Araújo, M. Sampaio, and M. Sampaio, “Impacts of Feature Selection on Predicting Machine Failures by Machine Learning Algorithms,” Applied Sciences, vol. 14, no. 8, 2024, Art. no. 3337, doi: 10.3390/app14083337.

E. Lwele, A. Shenfield, and C. E. Silva, “AI-Based Surrogate Models for the Food and Drink Manufacturing Industry: A Comprehensive Review,” Processes, vol. 13, no. 9, 2025, Art. no. 2929, doi: 10.3390/pr13092929.

K. G. Liakos, V. Athanasiadis, E. Bozinou, and S. I. Lalas, “Machine Learning for Quality Control in the Food Industry: A Review,” Foods, vol. 14, no. 19, 2025, Art. no. 3424, doi: 10.3390/foods14193424.

Z. C. Lipton, C. Elkan, and B. Naryanaswamy, “Optimal Thresholding of Classifiers to Maximize F1 Measure,” in European Conference, ECML PKDD 2014, Nancy, France, Sep. 15–19, 2014, pp. 225–239, doi: 10.1007/978-3-662-44851-9_15.

A. Aminzadeh, S. S. Karganroudi, S. Majidi, C. Dabompre, K. Azaiez, C. Mitride, and E. Sénéchal, “A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors,” Sensors, vol. 25, no. 4, 2025, Art. no. 1006, doi: 10.3390/s25041006.