Enhancing Predictive Maintenance of Industrial Assets Through Machine Diagnostic Parameter Grouping
DOI:
https://doi.org/10.24425/mper.2025.156149Abstract
This article examines the advancement of predictive maintenance (PdM) for industrial assets through an innovative methodology that categorises diagnostic parameters into coherent groups. Predictive maintenance constitutes a vital component in mitigating unforeseen downtime and improving operational efficiency within manufacturing settings. The authors recommend a centralised framework for PdM, effectively addressing the complexities arising from data saturation by numerous sensor nodes. The proposed methodology refines the predictive maintenance process by systematically organising diagnostic parameters based on their significance and interconnections, thereby enhancing its effectiveness and efficiency. The study utilises the KNIME software platform for comprehensive data analysis and validation of the proposed approach, demonstrating its practicality with datasets obtained from SCADA/MES systems. The results confirm the robustness and accessibility of the methodology, highlighting its potential applicability across various industrial sectors. Future research directions include the integration of advanced machine learning techniques and the exploration of the methodology’s relevance in diverse industries.References
Berthold, M.R., Cebron, N., Dill, F., Gabriel, T.R., Kötter, T., Meinl, T., Ohl, P., Sieb, C., Thiel, K., & Wiswedel, B. (2008). KNIME: The Konstanz Information Miner. In C. Preisach, H. Burkhardt, L. SchmidtThieme, & R. Decker (Eds.), Data Analysis, Machine Learning and Applications (pp. 319–326). Springer. DOI: 10.1007/978-3-540-78246-9_38
Calzavara, G., Oliosi, E., & Ferrari, G. (2021). A Timeaware Data Clustering Approach to Predictive Maintenance of a Pharmaceutical Industrial Plant. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 454–458. DOI: 10.1109/ICAIIC51459.2021.9415206
Chandna, M. (2024). Monitoring and Prediction of Supervision System for Industrial and Manufacturing Sectors Using Cloud Computing and Blockchain Technology. 2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE), 1–6. DOI: 10.1109/IC3SE62002.2024.10592888
EN 13306:2017 – Maintenance – Maintenance terminology. (2024). Retrieved 23 October 2024, from https://standards.iteh.ai/catalog/standards/cen/5af77559-ca38-483a-9310-823e8c517ee7/en-13306-2017
Gallego Garcia, S., & García, M. (2019). Industry 4.0 implications in production and maintenance management: An overview. Procedia Manufacturing, 41, 415-422. DOI: 10.1016/j.promfg.2019.09.027
Ibrahim, A.A.E.-H., Hashad, A.I., & Shawky, N.E.M. (2016). Performance Analysis of Various Open Source Tools on Four Breast Cancer Datasets using Ensemble Classifiers Techniques. International Journal of Engineering Research and Technology, 5 (3).
Jardine, A.K.S., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20 (7), 1483–1510. DOI: 10.1016/j.ymssp.2005.09.012
Karlsson, A., Bekar, E.T., & Skoogh, A. (2021). MultiMachine Gaussian Topic Modeling for Predictive Maintenance. IEEE Access, 9, 100063–100080. DOI: 10.1109/ACCESS.2021.3096387
Kaundal, R., Soni, S.K., & Rajguru, S. (2024). SCADAEnhanced Real-Time OEE Visualization Driving Industry 4.0 Advancements. 2024 International Conference on Smart Systems for Applications in Electrical Sciences (ICSSES), 1–6. DOI: 10.1109/ICSSES62373.2024.10561431
Kim, H.-G., Yoon, H.-S., Yoo, J.-H., Yoon, H.-I., & Han, S.-S. (2019). Development of Predictive Maintenance Technology for Wafer Transfer Robot using Clustering Algorithm. 2019 International Conference on Electronics, Information, and Communication (ICEIC), 1–4. DOI: 10.23919/ELINFOCOM.2019.8706485
Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent Maintenance Systems and Predictive Manufacturing. Journal of Manufacturing Science and Engineering, 142 (110805). DOI: 10.1115/1.4047856
Mallioris, P., Aivazidou, E., & Bechtsis, D. (2024). Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP Journal of Manufacturing Science and Technology, 50, 80–103. DOI: 10.1016/j.cirpj.2024.02.003
Metzger, A., Leitner, P., Ivanović, D., Schmieders, E., Franklin, R., Carro, M., Dustdar, S., & Pohl, K. (2015). Comparing and Combining Predictive Business Process Monitoring Techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45 (2), 276–290. IEEE Transactions on Systems, Man, and Cybernetics: Systems. DOI: 10.1109/TSMC. 2014.2347265
Mobley, R. K. (2002). An Introduction to Predictive Maintenance (2nd ed.). Butterworth-Heinemann.
Murtagh, F., & Legendre, P. (2014). Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Journal of Classification, 31 (3), 274–295. DOI: 10.1007/s00357-014-9161-z
Nunes, P., Santos, J., & Rocha, E. (2023). Challenges in predictive maintenance – A review. CIRP Journal of Manufacturing Science and Technology, 40, 53–67. DOI: 10.1016/j.cirpj.2022.11.004
PlantUML Web Server (2024). Retrieved 23 October 2024, from https://www.plantuml.com/plantuml/uml/SyfFKj2rKt3CoKnELR1Io4ZDoSa700003
