Forecasting Asset Health with Edge AI
Keywords:
Asset Health Forecasting, Edge Computing, Edge Intelligence, Fleet Resilience, Hybrid Sensor Fusion, Low-Latency Analytics, Maintenance Optimization, Predictive Maintenance, Prognostics, Risk-Informed Decision Making, Sensor Data Analytics, Structural Health Monitoring, Wind Turbine Monitoring.Abstract
This paper presents an edge-coupled intelligence system for rapid asset health forecasting and fleet-level resilience analysis in wind turbine operations. Health models are informed by local data processing on active device-level edge computing, so the system offers low latency, keeps asset health information private, and runs independently of cloud services. A local cluster of video, acoustic, and structural health sensors feeds a forecasting framework that supports multiple prognostic modeling approaches. The forecasts from a chosen method are translated into asset-level risk portfolios, which in turn guide operational planning under budgetary, regulatory, and availability constraints.
A second stage schedules planned maintenance under prediction uncertainty and quantifies the trade-off among downtime, cost, and future operational availability. Results show that maintenance can be scheduled with minimal added future risk or cost. Rules based on currently available health information tend to lower operating cost but at the expense of increased downtime. Because the approach keeps the overhead of enumerating predictions low, scheduling can use planning-scene distances drawn from a coarsely optimized set. Future work will calibrate assets to retain full-load availability.
References
1. Hsu, H. Y., Srivastava, G., Wu, H. T., & Chen, M. Y. (2020). Remaining useful life prediction based on state assessment using edge computing on deep learning. Computer Communications, 160, 91–100.
2. Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W. J., & Ducoffe, M. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92, 103678.
3. Kannam, N., Mangala, N., Kumar, R. P. R., Gupta, P., Kumar, D., & Raj, S. (2026). Evolutionary Genetic Optimization of Deep Learning for Scalable Cloud-Fog System. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1–6). IEEE. 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. https://doi.org/10.1109/otcon69332.2026.11630013
4. Rengasamy, D., Jafari, M., Rothwell, B., Chen, X., & Figueredo, G. P. (2020). Deep learning with dynamically weighted loss function for sensor-based prognostics and health management. Sensors, 20(3), 723.
5. Zhang, N., & Si, W. (2020). Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks. Reliability Engineering & System Safety, 203, 107094.
6. Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.
7. Nagabhyru, K. C., Gadi, A. L., Seenu, A., Davuluri, P. S. L. N., Segireddy, A. R., & Pamisetty, V. (2026). Towards Automated Financial Risk Scoring in Automotive Financing with Explainable Machine Learning. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1–6). IEEE. 2026 IEEE International Conference on AI Engineering and Innovations (AIEI). https://doi.org/10.1109/aiei69164.2026.11496822
8. Hafeez, T., Xu, L., & McArdle, G. (2021). Edge intelligence for data handling and predictive maintenance in IIoT. IEEE Access, 9, 49355–49371.
9. Wei, D., Han, T., Chu, F., & Zuo, M. J. (2021). Weighted domain adaptation networks for machinery fault diagnosis. Mechanical Systems and Signal Processing, 158, 107744.
10. Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021). Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering & System Safety, 215, 107864.
11. Segireddy, A. R., Davuluri, P. S. L. N., Mangala, N., & Nagubandi, A. R. (2026). Self-Healing Cloud-Native Quorum Systems: AI-Enabled DevOps for Zero-Downtime Financial Payments. In 2026 International Conference on NextGen Data Science and Analytics (ICNDSA) (pp. 1–9). IEEE. 2026 International Conference on NextGen Data Science and Analytics (ICNDSA). https://doi.org/10.1109/icndsa68777.2026.11652056
12. Zeng, F., Li, Y., Jiang, Y., & Song, G. (2021). A deep attention residual neural network-based remaining useful life prediction of machinery. Measurement, 176, 109642.
13. Kang, Z., Catal, C., & Tekinerdogan, B. (2021). Remaining useful life prediction of equipment in production lines using artificial neural networks. Sensors, 21(3), 932.
14. Krishnan, M., Yandamuri, U. S., Recharla, M., Challa, K., Kolla, S. H., & Valiki, S. (2026, June). Adaptive Machine Learning Ecosystem for Personalized Medicine and Digital Finance: Leveraging Azure Cloud, Data Engineering, and Workflow Automation. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1-6). IEEE.
15. Hesabi, H., Nourelfath, M., & Hajji, A. (2022). A deep learning predictive model for selective maintenance optimization. Reliability Engineering & System Safety, 219, 108191.
16. Yan, J., He, Z., & He, S. (2022). A deep learning framework for sensor-equipped machine health indicator construction and remaining useful life prediction. Computers & Industrial Engineering, 172, 108559.
17. Pugalenthi, K., Park, H., Hussain, S., & Raghavan, N. (2022). Remaining useful life prediction of lithium-ion batteries using neural networks with adaptive Bayesian learning. Sensors, 22(10), 3803.
18. Mahadevan, S. (2024). Clouding the Future: Innovating Towards Net-Zero Emissions. International Journal of Computing and Engineering, 6(2), 17-23.
19. Nieves Avendano, D., Vandermoortele, N., Soete, C., Moens, P., Ompusunggu, A. P., Deschrijver, D., & Van Hoecke, S. (2022). A semi-supervised approach with monotonic constraints for improved remaining useful life estimation. Sensors, 22(4), 1590.
20. de Beaulieu, M. H., Jha, M. S., Garnier, H., & Cerbah, F. (2022). Unsupervised remaining useful life prediction through long range health index estimation based on encoders-decoders. IFAC-PapersOnLine, 55(6), 718–723.
21. Wang, Y., Lei, Y., Li, N., Yan, T., & Si, X. (2023). Deep multisource parallel bilinear-fusion network for remaining useful life prediction of machinery. Reliability Engineering & System Safety, 231, 109006.
22. Zhu, J., Jiang, Q., Shen, Y., Xu, F., & Zhu, Q. (2023). Res-HSA: Residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery. Engineering Applications of Artificial Intelligence, 124, 106491.
23. Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry. (2026). Journal of European Economic History, 7(2), 68–88. https://doi.org/10.61336/jeeh/26-2-7 (Original work published as Engineering Intelligent Cloud-Native Data Ecosystems for Predictive Decision-Making in Industry)
24. Wen, L., Su, S., Wang, B., Ge, J., Gao, L., & Lin, K.-C. (2023). A new multi-sensor fusion with hybrid convolutional neural network with Wiener model for remaining useful life estimation. Engineering Applications of Artificial Intelligence, 126, 106934.
25. de Pater, I., & Mitici, M. (2023). Developing health indicators and RUL prognostics for systems with few failure instances and varying operating conditions using a LSTM autoencoder. Engineering Applications of Artificial Intelligence, 117, 105582.
26. M, S., Davuluri, P. S. L. N., Paramasamy, S., Yandamuri, U. S., Motamary, S., & N, Akshaya. (2026). AI-Enabled Business Process Reengineering for Agile and Data-Driven Organizations. In 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 (pp. 1–6). IEEE. 2026 5th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. https://doi.org/10.1109/otcon69332.2026.11629977
27. Barbuto, V., Savaglio, C., Chen, M., & Fortino, G. (2023). Disclosing edge intelligence: A systematic meta-survey. Big Data and Cognitive Computing, 7(1), 44.
28. Artiushenko, V., Lang, S., Lerez, C., Reggelin, T., & Hackert-Oschätzchen, M. (2024). Resource-efficient Edge AI solution for predictive maintenance. Procedia Computer Science, 232, 348–357.
29. Bala, A., Rashid, R. Z. J. A., Ismail, I., Oliva, D., Muhammad, N., Sait, S. M., Al-Utaibi, K. A., Amosa, T. I., & Memon, K. A. (2024). Artificial intelligence and edge computing for machine maintenance-review. Artificial Intelligence Review, 57(5), Article 119.
30. Li, Z., He, Q., & Li, J. (2024). A survey of deep learning-driven architecture for predictive maintenance. Engineering Applications of Artificial Intelligence, 133, 108285.
31. Mahadevan, S. (2026). Governed Serverless Automation Ecosystem for AI-Driven Enterprise Integration and Sustainable Cloud Operations. International Journal of Computer Information Systems and Industrial Management Applications, 18(16s), 1561-1570.
32. Wu, F., Wu, Q., Tan, Y., & Xu, X. (2024). Remaining useful life prediction based on deep learning: A survey. Sensors, 24(11), 3454.
33. Shoorkand, H. D., 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.
34. Chen, L., Gao, H., Guo, L., Liang, J., & Peng, L. (2025). Remaining useful life prediction of machinery using federated public feature representation in edge-cloud collaboration architecture. Engineering Applications of Artificial Intelligence, 143, 110059.
35. Wang, C., Jiang, W., Shi, L., & Zhang, L. (2025). Rolling bearing remaining useful life prediction using deep learning based on high-quality representation. Scientific Reports, 15, 8228.
36. Wang, F., Liu, A., Qu, C., Xiong, R., & Chen, L. (2025). A deep-learning method for remaining useful life prediction of power machinery via dual-attention mechanism. Sensors, 25(2), 497.
37. Al-Refaie, A., Al-atrash, M., & Lepkova, N. (2025). Prediction of the remaining useful life of a milling machine using machine learning. MethodsX, 14, 103195.
38. A, R., Maheswari, N. U., Umamaheswari, P., Josephson, P. J., Mangalampalli, B. M., & Gudada, P. V. (2026). Tree-Structured Regularization Driven CNN Model for Liver Disease Diagnosis and Classification Using Ultrasound Imaging. In 2026 International Conference on Intelligent and Sustainable Electronics & Computing Technologies (INSECT) (pp. 1–6). IEEE. 2026 International Conference on Intelligent and Sustainable Electronics & Computing Technologies (INSECT). https://doi.org/10.1109/insect68872.2026.11663742
39. Lamdjad, B. (2025). Edge AI-driven adaptive maintenance for manufacturing systems: Improving decision-making and reducing energy consumption. Journal of Intelligent Manufacturing and Special Equipment, 6(3), 312–326.
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 Rama chandra nampalli (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The American Online Journal of Science and Engineering (AOJSE) is an open-access journal, and all published articles are made freely available to readers worldwide under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license governs the use, sharing, adaptation, and distribution of all content published in AOJSE and is designed to promote the broadest possible dissemination and reuse of scholarly research.
Creative Commons Attribution 4.0 International License (CC BY 4.0)
Under the Creative Commons Attribution 4.0 International License, any user is free to share, copy, and redistribute the published material in any medium or format, and to adapt, remix, transform, and build upon the material for any purpose, including commercial use, provided that appropriate credit is given to the original authors and source. The license cannot be revoked as long as the terms are followed.
When using or redistributing content published in AOJSE, users must provide proper attribution by citing the original authors' names, the article title, the journal name (AOJSE), the volume and issue number, the year of publication, and the DOI or URL of the article. Users must also indicate if any changes were made to the original work. Attribution must be provided in a reasonable manner but must not suggest that the authors or AOJSE endorse the user or their use of the material.
Author Rights and Copyright Retention
AOJSE respects and upholds the intellectual property rights of all contributing authors. Authors who publish in AOJSE retain full copyright over their work. By submitting a manuscript to AOJSE, authors grant the journal a non-exclusive, worldwide, royalty-free license to publish, reproduce, distribute, publicly display, and archive the article in print and electronic formats. This license allows AOJSE to make the article freely accessible to readers globally while ensuring that the authors remain the rightful owners of their work.
Authors are permitted to deposit their published articles in institutional repositories, personal websites, academic networking platforms, and preprint servers, provided that the original publication in AOJSE is acknowledged and properly cited. Authors may also reuse their published content in subsequent works, presentations, teaching materials, and grant applications without requiring prior permission from the journal.
Third-Party Content
Authors are solely responsible for obtaining written permission to reproduce any third-party material, including figures, tables, images, data, or excerpts from other publications, included in their manuscript. Evidence of such permissions must be provided to the editorial office upon request. AOJSE does not assume any responsibility for copyright infringement arising from the unauthorized inclusion of third-party content in published articles.
Permitted Uses
Under the CC BY 4.0 license, the following uses of AOJSE published content are freely permitted without prior written permission from the journal or authors, provided that proper attribution is given. Users may read, download, print, and distribute articles for personal, educational, or research purposes. Researchers may reuse data, figures, and findings for meta-analyses, systematic reviews, and secondary research. Educators may incorporate published articles into course materials, syllabi, and academic presentations. Journalists, policymakers, and practitioners may reference and cite published findings for professional and public interest purposes.
Prohibited Uses
While the CC BY 4.0 license is broad and permissive, users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Users may not misrepresent the original authorship of published content or imply endorsement by the original authors or AOJSE without explicit consent. Plagiarism, data fabrication, and any form of academic misconduct in the reuse of published material are strictly prohibited and are a violation of publication ethics standards.
Institutional and Repository Deposit Policy
AOJSE fully supports self-archiving and green open access. Authors are encouraged to deposit the final published version of their article, also known as the version of record, in institutional repositories, subject repositories, and open-access databases immediately upon publication. There is no embargo period. Authors should always link back to the original article on the AOJSE website and include the full citation and DOI when depositing their work in any repository.
Digital Preservation and Archiving
AOJSE is committed to the long-term digital preservation of all published content to ensure its permanent availability and accessibility. The journal utilizes the Open Journal Systems (OJS) platform, which supports internationally recognized digital archiving standards. AOJSE encourages participation in archiving networks such as LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled LOCKSS) to safeguard published articles against data loss and ensure continued access for future generations of researchers and readers.
Disclaimer
The views, opinions, findings, and conclusions expressed in articles published in AOJSE are solely those of the authors and do not necessarily reflect the official position, policy, or views of the journal, its editorial board, or its affiliated organizations. AOJSE makes every effort to ensure the accuracy and integrity of published content but does not accept legal responsibility for any errors, omissions, or claims arising from the use of information published in the journal.
Contact for Licensing Inquiries
For any questions regarding the licensing terms, copyright, or permitted use of content published in AOJSE, please contact the editorial office through the journal website at https://aojse.org. The editorial team will be happy to assist authors, readers, and institutions with any licensing-related queries.
