Adaptive Framework for Industrial Maintenance Intelligence
Keywords:
Cognitive Technology, Smart Maintenance, Use-Centric Control, Decision-Making, Industrial Automation, Operational Stability, Semantic System Integration, Data Governance, Data Management, Communication Ontology, Scheduling, Resource-Aiguity.Abstract
Maintenance and control of industrial processes and systems have reached a degree of complexity that presents overwhelming challenges. New approaches integrating relevant theory, data, equipment, infrastructure, and services to optimise planning and decision-making represent an appealing solution. The Integrated Industrial Cognition (IIC) service framework encompasses not only data, but dedicated bandwidth for rapid interactions, an umbrella of sophisticated Industrial Artificial Intelligence (IAI) with user-configured analyses, algorithms and models, enabling such complex support in a distributive architecture. Data from operations and condition monitoring informs Health Management processes that support Smart Maintenance, while the Logical-Centric Control IAI-Architecture augments classical Process Control with Lower-Level Cognition for anomaly detection and Higher-Level Cognition for fault tolerance.
The synthesis of these support systems allows Smart Maintenance and Operational Stability to be described using common terminology, a common concept suite, made possible by Position-specific Representation, common structured information space, and proposed tools for Resource-Aiguity and Scheduling under Uncertainty: the RAS-ToolKit. Common terms and concepts simplify user-configured analyses, algorithms, and models that embody the sophisticated processes and services required for optimal Smart Maintenance and Operational Stability.
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1. 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.
2. Benatia, M. A., Hafsi, M., & Ben Ayed, S. (2025). A continual learning approach for failure prediction under non-stationary conditions: Application to condition monitoring data streams. Computers & Industrial Engineering, 204, 111049.
3. Bondoc, A. E., Tayefeh, M., & Barari, A. (2022). Learning phase in a LIVE Digital Twin for predictive maintenance. Autonomous Intelligent Systems, 2, 13.
4. Davuluri, P. S. L. (2023). AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems (December 15, 2023).
5. Cakir, M., Guvenc, M. A., & Mistikoglu, S. (2021). The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Computers & Industrial Engineering, 151, 106948.
6. Chen, C., Liu, Y., Wang, S., Sun, X., Di Cairano-Gilfedder, C., Titmus, S., & others. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581–594.
7. Dehghan Shoorkand, H., Nourelfath, M., & Hajji, A. (2024). A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning. Reliability Engineering & System Safety, 241, 109707.
8. Dehghan Shoorkand, H., Nourelfath, M., & Hajji, A. (2024). A hybrid deep learning approach to integrate predictive maintenance and production planning for multi-state systems. Journal of Manufacturing Systems, 74, 397–410.
9. Kolla, S. K., & Bandi, V. D. V. K. (2025). Autonomous Clinical Monitoring Platforms Using Reinforcement Learning and Deep Neural Networks. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(6), 13300-13313.
10. Einabadi, B., Mahmoodjanloo, M., Baboli, A., & Rother, E. (2023). Dynamic predictive and preventive maintenance planning with failure risk and opportunistic grouping considerations: A case study in the automotive industry. Journal of Manufacturing Systems, 69, 292–310.
11. Feng, Q., Zhang, Y., Sun, B., Guo, X., Fan, D., Ren, Y., Song, Y., & Wang, Z. (2023). Multi-level predictive maintenance of smart manufacturing systems driven by digital twin: A matheuristics approach. Journal of Manufacturing Systems, 68, 443–454.
12. Gao, Z., Yao, J., He, Y., Dui, H., Liang, Z., & Li, J. (2025). Remaining useful life prediction and preventive maintenance approach for multistate manufacturing systems with feedstock heterogeneity. Reliability Engineering & System Safety, 264, 111352.
13. Geng, S., & Wang, X. (2022). Predictive maintenance scheduling for multiple power equipment based on data-driven fault prediction. Computers & Industrial Engineering, 164, 107898.
14. Kolla, S. H., & Mangala, N. (2025). DESIGNING AUTONOMOUS LLM AGENT FRAMEWORKS USING GEN AI PIPELINES TO ENHANCE CUSTOMER SERVICE MANAGEMENT AND KNOWLEDGE WORKFLOWS. Lex Localis-Journal of Local Self-Government, 23 (S6), 9719–9733.
15. Görür, O. C., Yu, X., & Sivrikaya, F. (2021). Integrating predictive maintenance in adaptive process scheduling for a safe and efficient industrial process. Applied Sciences, 11(11), 5042.
16. Han, S., Awasthi, U., & Bollas, G. M. (2025). Physics-informed symbolic regression for tool wear and remaining useful life predictions in manufacturing. Journal of Manufacturing Systems, 80, 734–748.
17. Hassan, M., Svadling, M., & Björsell, N. (2024). Experience from implementing digital twins for maintenance in industrial processes. Journal of Intelligent Manufacturing, 35, 875–884.
18. He, J., Khebbache, S., Anjos, M. F., & Hadji, M. (2023). Optimization of maintenance for complex manufacturing systems using stochastic remaining useful life prognostics. Computers & Industrial Engineering, 182, 109348.
19. Huang, C., Bu, S., Lee, H. H., Chan, C. H., Kong, S. W., & Yung, W. K. C. (2024). Prognostics and health management for predictive maintenance: A review. Journal of Manufacturing Systems, 75, 78–101.
20. Jiang, Y., Dai, P., Peng, F., Zhong, R. Y., & others. (2022). A²-LSTM for predictive maintenance of industrial equipment based on machine learning. Computers & Industrial Engineering, 172, 108560.
21. Lambán, M. P., Morella, P., Royo, J., & Sánchez, J. C. (2022). Using Industry 4.0 to face the challenges of predictive maintenance: A key performance indicators development in a cyber physical system. Computers & Industrial Engineering, 171, 108400.
22. Li, Y., He, Y., Liao, R., Zheng, X., & Dai, W. (2022). Integrated predictive maintenance approach for multistate manufacturing system considering geometric and non-geometric defects of products. Reliability Engineering & System Safety, 228, 108793.
23. Ma, S., Flanigan, K. A., & Bergés, M. (2024). State-of-the-art review and synthesis: A requirement-based roadmap for standardized predictive maintenance automation using digital twin technologies. Advanced Engineering Informatics, 62, 102800.
24. Kolla, S. K., & Mangalampalli, B. M. (2024). Edge-Based Deep Learning Systems for Point-of-Care Diagnostic Intelligence. Journal of Neonatal Surgery, 13(1), 2387-2399.
25. Meitz, L., Senge, J., Wagenhals, T., Schöler, T., Hähner, J., Edinger, J., & Krupitzer, C. (2025). A literature review framework and open research challenges for predictive maintenance in Industry 4.0. Computers & Industrial Engineering, 206, 111193.
26. Mi, S., Feng, Y., Zheng, H., Wang, Y., Gao, Y., & Tan, J. (2021). Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework. Journal of Manufacturing Systems, 58, 329–345.
27. Mohan, R., Preetha Roselyn, J., 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.
28. Murtaza, A. A., Saher, A., Zafar, M. H., Moosavi, S. K. R., Aftab, M. F., & Sanfilippo, F. (2024). Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study. Results in Engineering, 24, 102935.
29. Pech, M., Vrchota, J., & Bednář, J. (2021). Predictive maintenance and intelligent sensors in smart factory: Review. Sensors, 21(4), 1470.
30. Prioli, J. P. J., Altinpulluk, N. B., Rickli, J. L., & Yildirim, M. (2025). Self-adaptive production performance monitoring framework under different operating regimes. Journal of Manufacturing Systems, 80, 380–394.
31. Singh, R. R., Bhatti, G., Kalel, D., Vairavasundaram, I., & Alsaif, F. (2023). Building a digital twin powered intelligent predictive maintenance system for industrial AC machines. Machines, 11(8), 796.
32. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.
33. Taşcı, B., Omar, A., & Ayvaz, S. (2023). Remaining useful lifetime prediction for predictive maintenance in manufacturing. Computers & Industrial Engineering, 184, 109566.
34. Ucar, A., Karakose, M., & Kırımça, N. (2024). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Applied Sciences, 14(2), 898.
35. Zhong, D., Xia, Z., Zhu, Y., & Duan, J. (2023). Overview of predictive maintenance based on digital twin technology. Heliyon, 9(4), e14534.
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