Manufacturing Technology 2026, 26(3):380-394 | DOI: 10.21062/mft.2026.033

Mathematical Analysis of Predictive Maintenance Strategies for Enhanced Manufacturing Efficiency

Varsha Haridas Sadrani ORCID...1, Lowlesh Nandkishor Yadav ORCID...2, Jan Blata3, Shradhesh Rajuji Marve ORCID...4, Ankita Rekkaward5, B Swarna6,7, Robert Cep ORCID...8
1 Department of Mathematics, Rajiv Gandhi College of Engineering Research and Technology, Chandrapur 442403, India
2 Department of Computer Science and Engineering, Tulsiramji Gaikwad Patil College of Engineering and Technology, Nagpur 441108, India
3 Department of Machine and Industrial Design, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
4 Department of Civil Engineering, Rajiv Gandhi College of Engineering Research and Technology, Chandrapur 442403, India
5 Department of Electronics and Telecommunication Engineering, Swaminarayan Siddhant Institute of Technology, Nagpur 441501, India
6 Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India
7 University Centre for Research & Development, Chandigarh University, Mohali 140413, India
8 Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic

In manufacturing, the demand is always for better proficiency, less operational costs, and greater effectiveness. The key in achieving these requirements through adept handling is, in fact, maintenance of machines and devices. Combinations of regular maintenance schemes, preventive and curative methods, usually tend to swing between unnecessary maintenance jobs and unexpected equipment failures. This situation calls for a need to develop a more sophisticated approach, which gives rise to Predictive Maintenance (PdM). PdM is different from the rest because it forecasts changes that lead to failure in the equipment even before they seem probable, preparing the ground for pre-emptive measures, thereby reducing downtime, and reducing maintenance costs on a really large scale. However, the introduction of PdM does not come without corresponding challenges, which include: Data compilation and management within PdM systems, difficulty in modeling machinery's nonlinear dynamics, difficulties involved in integrating PdM systems into traditional operational pipelines of manufacturing entities, as well as justification of return on investments. To these issues, this paper adopts sophisticated mathematical models that have been selected carefully for their capabilities to handle bulk data, decipher intricate interrelations, and accurately predict future failures. Examples include Time Series Analysis: ARIMA and SARIMA use sensor temporal patterns; Survival Analysis, using Cox Proportional Hazards model, to measure machinery failure survival horizons; and advanced Machine Learning algorithms such as Stochastic Forests and Gradient Boosting Machines known for their nonlinear data acuity and insight into feature significance levels. Empirical validation of the model across diverse data samples reveals that the proposed model excels on all metric levels by achieving an 8.5% improvement in predictive precision, an 8% increase in accuracy, 4.9% boost in recall, 9.5 times faster velocity, a 4.5 increment in AUC, and an impressive 10.4% shot in specificity over what is available today. The work resolves the tensions between theory and real-life application while setting a new benchmark in predictive maintenance, thereby heralding a paradigm shift in the levels of manufacturing efficiency and reliability sets.

Keywords: Predictive Maintenance, Time Series Analysis, Machine Learning, Survival Analysis, Manufacturing Efficiency, Scenarios
Grants and funding:

This article was co-funded by the European Union under the REFRESH – Research Excellence For RE-gion Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition and has been done in connection with project Students Grant Competition SP2024/087 „Specific Research of Sustainable Manufacturing Technologies” financed by the Ministry of Education, Youth and Sports and Faculty of Mechanical Engineering V©B-TUO

Received: June 13, 2025; Revised: May 25, 2026; Accepted: May 27, 2026; Prepublished online: June 15, 2026; Published: June 29, 2026  Show citation

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Haridas Sadrani V, Nandkishor Yadav L, Blata J, Marve SR, Rekkaward A, Swarna B, Cep R. Mathematical Analysis of Predictive Maintenance Strategies for Enhanced Manufacturing Efficiency. Manufacturing Technology. 2026;26(3):380-394. doi: 10.21062/mft.2026.033.
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    References

    1. FU, J., PEYGHAMI, S., NÚÑEZ, A., BLAABJERG, F., & DE SCHUTTER, B. A tractable failure probability prediction model for predictive maintenance scheduling of large-scale modular-multilevel-converters. IEEE Transactions on Power Electronics, Vol. 38, No. 5, 2023, pp. 6533-6544. https://doi.org/10.1109/TPEL.2023.3241317 Go to original source...
    2. MANCHADI, O., BEN-BOUAZZA, F.-E., & JIOUDI, B. Predictive maintenance in healthcare system: A survey. IEEE Access, Vol. 11, 2023, pp. 61313-61330. https://doi.org/10.1109/ACCESS.2023.3287490 Go to original source...
    3. ZENG, J., & LIANG, Z. A deep Gaussian process approach for predictive maintenance. IEEE Transactions on Reliability, Vol. 72, No. 3, 2023, pp. 916-933. https://doi.org/10.1109/TR.2022.3199924 Go to original source...
    4. BURMEISTER, N., FREDERIKSEN, R.D., HØG, E., & NIELSEN, P. Exploration of production data for predictive maintenance of industrial equipment: A case study. IEEE Access, Vol. 11, 2023, pp. 102025-102037. https://doi.org/10.1109/ACCESS.2023.3315842 Go to original source...
    5. HEIDEN, P.Z., PRIEFER, J., & BEVERUNGEN, D. Predictive maintenance on the energy distribution grid-Design and evaluation of a digital industrial platform in the context of a smart service system. IEEE Transactions on Engineering Management, Vol. 71, 2024, pp. 3641-3655. https://doi.org/10.1109/TEM.2024.3352819 Go to original source...
    6. CHEN, C., SHI, J., SHEN, M., FENG, L., & TAO, G. A predictive maintenance strategy using deep learning quantile regression and kernel density estimation for failure prediction. IEEE Transactions on Instrumentation and Measurement, Vol. 72, 2023, Art no. 3506512, pp. 1-12. https://doi.org/10.1109/TIM.2023.3240208 Go to original source...
    7. BINDER, M., MEZHUYEV, V., & TSCHANDL, M. Predictive maintenance for railway domain: A systematic literature review. IEEE Engineering Management Review, Vol. 51, No. 2, 2023, pp. 120-140. https://doi.org/10.1109/EMR.2023.3262282 Go to original source...
    8. WANG, H., ZHANG, W., YANG, D., & XIANG, Y. Deep-learning-enabled predictive maintenance in industrial internet of things: Methods, applications, and challenges. IEEE Systems Journal, Vol. 17, No. 2, 2023, pp. 2602-2615. https://doi.org/10.1109/JSYST.2022.3193200 Go to original source...
    9. SPANGLER, R.M., AGARWAL, V., & COLE, D.G. A hybrid reliability model using generalized renewal processes for predictive maintenance in nuclear power plant circulating water systems. IEEE Access, Vol. 11, 2023, pp. 136726-136740. https://doi.org/10.1109/ACCESS.2023.3338716 Go to original source...
    10. AZARI, M.S., FLAMMINI, F., SANTINI, S., & CAPORUSCIO, M. A systematic literature review on transfer learning for predictive maintenance in Industry 4.0. IEEE Access, Vol. 11, 2023, pp. 12887-12910. https://doi.org/10.1109/ACCESS.2023.3239784 Go to original source...
    11. FASSI, Y., HEIRIES, V., BOUTET, J., & BOISSEAU, S. Toward physics-informed machine-learning-based predictive maintenance for power converters-A review. IEEE Transactions on Power Electronics, Vol. 39, No. 2, 2024, pp. 2692-2720. https://doi.org/10.1109/TPEL.2023.3328438 Go to original source...
    12. SUJATI, W., YUDOKO, G., & OKDINAWATI, L. Transactional and transformational leadership styles and their impact on employees' acceptance of predictive maintenance analytics: Evidence from an Indonesian mining company. IEEE Access, Vol. 11, 2023, pp. 49675-49688. https://doi.org/10.1109/ACCESS.2023.3277006 Go to original source...
    13. SUAWA, P.F., HALBINGER, A., JONGMANNS, M., & REICHENBACH, M. Noise-robust machine learning models for predictive maintenance applications. IEEE Sensors Journal, Vol. 23, No. 13, 2023, pp. 15081-15092. https://doi.org/10.1109/JSEN.2023.3273458 Go to original source...
    14. CHEN, C., TAO, G., SHI, J., SHEN, M., & ZHU, Z.H. A lithium-ion battery degradation prediction model with uncertainty quantification for its predictive maintenance. IEEE Transactions on Industrial Electronics, Vol. 71, No. 4, 2024, pp. 3650-3659. https://doi.org/10.1109/TIE.2023.3274874 Go to original source...
    15. SINGH, S., BATHERI, R., & DIAS, J. Predictive analytics: How to improve availability of manufacturing equipment in automotive firms. IEEE Engineering Management Review, Vol. 51, No. 4, 2023, pp. 157-168. https://doi.org/10.1109/EMR.2023.3288669 Go to original source...
    16. BORGHESI, A., BURRELLO, A., & BARTOLINI, A. ExaMon-X: A predictive maintenance framework for automatic monitoring in industrial IoT systems. IEEE Internet of Things Journal, Vol. 10, No. 4, 2023, pp. 2995-3005. https://doi.org/10.1109/JIOT.2021.3125885 Go to original source...
    17. CHEN, Z., GAO, Y., & LIANG, J. LOPdM: A low-power on-device predictive maintenance system based on self-powered sensing and TinyML. IEEE Transactions on Instrumentation and Measurement, Vol. 72, 2023, Art no. 2525213, pp. 1-13. https://doi.org/10.1109/TIM.2023.3308251 Go to original source...
    18. RAHMAN, N.H.A., HASIKIN, K., RAZAK, N.A.A., AL-ANI, A.K., ANNI, D.J.S., & MOHANDAS, P. Medical device failure predictions through AI-driven analysis of multimodal maintenance records. IEEE Access, Vol. 11, 2023, pp. 93160-93179. https://doi.org/10.1109/ACCESS.2023.3309671 Go to original source...
    19. TEOH, Y.K., GILL, S.S., & PARLIKAD, A.K. IoT and fog-computing-based predictive maintenance model for effective asset management in Industry 4.0 using machine learning. IEEE Internet of Things Journal, Vol. 10, No. 3, 2023, pp. 2087-2094. https://doi.org/10.1109/JIOT.2021.3050441 Go to original source...
    20. YANG, L., CHEN, Y., & MA, X. A state-age-dependent opportunistic intelligent maintenance framework for wind turbines under dynamic wind conditions. IEEE Transactions on Industrial Informatics, Vol. 19, No. 10, 2023, pp. 10434-10443. https://doi.org/10.1109/TII.2023.3240727 Go to original source...
    21. DUI, H., DONG, X., CHEN, L., & WANG, Y. IoT-enabled fault prediction and maintenance for smart charging piles. IEEE Internet of Things Journal, Vol. 10, No. 23, 2023, pp. 21061-21075. https://doi.org/10.1109/JIOT.2023.3285206 Go to original source...
    22. OLIOSI, E., CALZAVARA, G., & FERRARI, G. On sensor data clustering for machine status monitoring and its application to predictive maintenance. IEEE Sensors Journal, Vol. 23, No. 9, 2023, pp. 9620-9639. https://doi.org/10.1109/JSEN.2023.3260314 Go to original source...
    23. LOKMAN, A., RAMASAMY, R.K., & TING, C.-Y. Scheduling and predictive maintenance for smart toi-let. IEEE Access, Vol. 11, 2023, pp. 17983-17999. https://doi.org/10.1109/ACCESS.2023.3241942 Go to original source...
    24. LI, Y., SHI, Y., ZHANG, Z., LU, N., WANG, X., & ZIO, E. Condition-based maintenance for performance degradation under nonperiodic unreliable inspections. IEEE Transactions on Artificial Intelligence, Vol. 4, No. 4, 2023, pp. 709-721. https://doi.org/10.1109/TAI.2022.3197680 Go to original source...
    25. JOVICIC, E., PRIMORAC, D., CUPIC, M., & JOVIC, A. Publicly available datasets for predictive maintenance in the energy sector: A review. IEEE Access, Vol. 11, 2023, pp. 73505-73520. https://doi.org/10.1109/ACCESS.2023.3295113 Go to original source...
    26. Pandey, S., Kumar, R., Kumar Singh, A., Cep, R., Singh, P., Kumar Pandey, M., & Swarna, B. (2026). Machine Learning-Based Predictive Modelling of EDM and EAM-V Processes for Performance Analy-sis. Manufacturing Technology Journal, 26(2), 220-232. doi: 10.21062/mft.2026.017 Go to original source...
    27. Charde, M.M., Najan, T.P., Cepova, L., Jadhav, A.D., Rash-inkard, N.S., & Samal, S.P. (2025). Predictive Modelling of Surface Roughness in Grinding Operations Using Machine Learning Techniques. Manufacturing Technology Journal, 25(1), 14-23. doi: 10.21062/mft.2025.006 Go to original source...
    28. Cheng, F., & Zheng, W. (2025). Research on Stator Thermal Fault Detection of Steam Turbine Generator Based on Improved Transformer and Gaussian Mixture Model. Manufacturing Technology Journal, 25(4), 448-454. doi: 10.21062/mft.2025.051 Go to original source...
    29. BURANDE, D. V., KALITA, K., GUPTA, R., KUMAR, A., CHOHAN, J. S., & KUMAR, D. Machine learning metamodels for thermo-mechanical analysis of friction stir welding. International Journal on Interactive Design and Manufacturing, Vol. 19, No. 1, 2025, pp. 597-615. https://doi.org/10.1007/s12008-024-01871-6 Go to original source...
    30. SARKER, B., CHAKRABORTY, S., ÈEP, R., & KALITA, K. Development of optimized ensemble machine learning-based prediction models for wire electrical discharge machining processes. Scientific Reports, Vol. 14, No. 1, 2024, Article 23299. https://doi.org/10.1038/s41598-024-74291-x Go to original source...
    31. GANESH, N., JOSHI, M., DUTTA, P., & KALITA, K. PSO-tuned support vector machine metamodels for assessment of turbulent flows in pipe bends. Engineering Computations, Vol. 37, No. 3, 2020, pp. 981-1001. https://doi.org/10.1108/EC-05-2019-0244 Go to original source...

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