Connected Fleet Intelligence: Edge-Centric Analytics and Computer Vision for Predictive Manufacturing and Asset Resilience
DOI:
https://doi.org/10.15662/IJARCST.2023.0605016Keywords:
Connected Fleet Intelligence, Edge AI Analytics, Computer Vision for Manufacturing, Predictive Maintenance, Industrial Asset Resilience, Real-Time Fleet Monitoring, Edge Computing in Industry 4.0, Machine Health Diagnostics, AI-Driven Asset Management, Smart Manufacturing OperationsAbstract
Data fusion, edge analytics, computer vision, and predictive manufacturing converge to enhance asset resilience through low-latency, trustworthy decision making. The transition from a reactive to predictive approach to maintenance of industrial assets such as machinery and equipment presents an opportunity to improve asset resilience and avoid unscheduled production downtimes with significant cost implications. Addressing three interrelated challenges—identifying the optimal frequency for maintenance activities; defining asset reliability indicators; and automatic detection of defects—contributions are made to applied predictive maintenance from standalone visual inspection systems in addition to data-driven insights. The Delmia Ortems neural network-based model predicting optimal spare parts ordering for an automotive plant is used. The condition monitoring dataset from the NASA C-MAPSS record set serves as an example to define remaining useful life indicators of turbines and their bearings monitored by vibration sensors. A use case presents a crowdsourced computer vision application for visual defects on city assets.
Evidence-based framing encompasses data governance with a business glossary, maturity assessment, and explainability, ensuring stakeholders comprehend, trust, and remain involved with the developed solutions. Reliability indicators assist industrial asset portfolios with both dashboard-style visualization of status and remaining service time versus maintenance. Integration of Vision AI within City Asset Integrity involves a visual damage report from citizens supported by edge-centric computer vision processing and potential triangulation-based coordinates for the defects detected. All solutions are designed for low-latency deployment.
References
1. Ucar, A., Karakose, M., & Kırımça, N. (2023). Artificial intelligence for predictive maintenance applications: Key components, trustworthiness, and future trends. Applied Sciences, 14(2), 898.
2. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
3. Jayasinghe, U., Lee, G. M., Um, T. W., & Shi, Q. (2023). Machine learning and digital twins for predictive maintenance in Industry 4.0: A systematic review. Journal of Manufacturing Systems, 69, 473–489.
4. Stadnicka, D., Sęp, J., Amadio, R., Mazzei, D., Tyrovolas, M., Stylios, C., Carreras-Coch, A., Merino, J. A., Żabiński, T., & Navarro, J. (2022). Industrial needs in the fields of artificial intelligence, Internet of Things and edge computing. Sensors, 22(12), 4501.
5. Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2023). Artificial intelligence and machine learning applications in smart production: Progress, trends, and challenges. Sustainability, 15(4), 3353.
6. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).
7. Khan, W. Z., Rehman, M. H., Zangoti, H. M., Afzal, M. K., Armi, N., & Salah, K. (2023). Industrial Internet of Things: Recent advances, enabling technologies, and open challenges. Computers & Electrical Engineering, 107, 108623.
8. Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2022). Design and development of an edge-computing platform towards 5G technology adoption for improving equipment predictive maintenance. Procedia Computer Science, 200, 611–619.
9. Bousdekis, A., Apostolou, D., Mentzas, G., & Stojanovic, N. (2023). Predictive maintenance in the era of Industry 4.0: State of the art and future directions. Computers in Industry, 146, 103849.
10. Siva Hemanth Kolla, Raghunath Loganathan. (2023). Cloud-Native Deep Learning Architectures For Secure Generative AI Deployment In Enterprise Workflow Platforms. Journal of International Crisis and Risk Communication Research , 603–618. https://doi.org/10.63278/jicrcr.vi.3786
11. Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Suman, R. (2023). Understanding the adoption of artificial intelligence in manufacturing and industrial automation. Advanced Industrial and Engineering Polymer Research, 6(4), 289–301.
12. Bala, A., Jusoh, A. R. Z., Ismail, I., Oliva, D., Muhammad, N., Sait, S. M., Al-Utaibi, K. A., Amosa, T. I., & Memon, K. A. (2023). Artificial intelligence and edge computing for machine maintenance: A review. Artificial Intelligence Review, 57(5), 119.
13. Li, Y., Wang, X., Ding, K., & Feng, S. (2023). Explainable artificial intelligence for smart manufacturing systems: A review. Robotics and Computer-Integrated Manufacturing, 84, 102582.
14. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
15. Thakkar, D., & Kumar, R. (2023). AI-driven predictive maintenance for industrial assets using edge computing and machine learning. Journal of Research and Applied Sciences and Biotechnology, 3, 363–367.
16. Djenouri, Y., Srivastava, G., Lin, J. C. W., & Chatterjee, P. (2023). Edge intelligence for industrial IoT: Architectures, applications, and research challenges. IEEE Internet of Things Magazine, 6(2), 40–46.
17. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
18. Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2023). Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data-based feedback and coordination. Computer Networks, 226, 109704.
19. Hector, I., Rodríguez, J., & Fernández, P. (2023). Predictive maintenance in Industry 4.0: A survey of planning and scheduling perspectives. Journal of Intelligent Manufacturing, 35(4), 1453–1478.
20. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
21. Mistry, M., Gupta, N., & Patel, R. (2023). Deep learning-enabled anomaly detection in industrial cyber-physical systems. Expert Systems with Applications, 223, 119905.
22. Nunes, P., Santos, J., & Rocha, E. (2023). Challenges in predictive maintenance: A review. CIRP Journal of Manufacturing Science and Technology, 40, 53–67.
23. Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., & Edinbarough, I. (2023). State of Industry 5.0—Analysis and identification of current research trends. Applied System Innovation, 6(1), 27.
24. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.
25. Borgia, E., Conti, M., & Dargahi, T. (2023). Secure edge computing for industrial Internet of Things environments. Future Internet, 15(6), 205.
26. Chen, X., Zhang, Y., Wang, H., & Li, J. (2023). Edge intelligence for predictive maintenance in smart manufacturing systems. IEEE Internet of Things Journal, 10(14), 12188–12203.
27. Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.
28. Zheng, P., Wang, H., Sang, Z., Zhong, R. Y., Liu, Y., Liu, C., Mubarok, K., Yu, S., & Xu, X. (2023). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 18(2), 23.
29. Li, S., Xu, L. D., & Zhao, S. (2023). The Internet of Things: A survey of industrial applications and future directions. Information Systems Frontiers, 25(1), 155–176.
30. Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Rocha-Lona, L., & Tortorella, G. (2023). Exploring Industry 4.0 technologies for predictive maintenance and operational excellence. Production Planning & Control, 34(10), 945–960.
31. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
32. Bagheri, B., Yang, S., Kao, H. A., & Lee, J. (2023). Cyber-physical systems architecture for self-aware manufacturing and predictive operations. Manufacturing Letters, 36, 61–66.
33. Wang, K., Yang, Y., Ren, J., & Zhang, L. (2023). Federated learning enabled predictive maintenance for industrial IoT systems. IEEE Transactions on Industrial Informatics, 19(8), 8471–8482.
34. Divya, V., & Bandi, V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 78.
35. Kumar, A., Singh, R., & Sharma, P. (2023). Edge AI-enabled condition monitoring and predictive maintenance for smart factories. Journal of Manufacturing Systems, 68, 134–148.
36. Tang, C., Liu, F., & Zhang, Y. (2023). Vision transformer-based defect inspection for intelligent manufacturing systems. Engineering Applications of Artificial Intelligence, 124, 106582.
37. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
38. Alcaraz, C., & Lopez, J. (2023). Cyber resilience in industrial control and manufacturing systems: Challenges and opportunities. Computers & Security, 128, 103171.
39. Zhang, T., Liu, Q., Chen, Y., & Zhou, X. (2023). Deep learning-based fault diagnosis and predictive maintenance for industrial cyber-physical systems. Computers in Industry, 148, 103895.
40. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2023). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 67, 530–535.
41. Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.
42. Chhetri, S. R., Faezi, S., Canedo, A., Al Faruque, M. A., & Wan, J. (2023). IoT and edge computing for asset monitoring and predictive analytics in smart industries. IEEE Transactions on Industrial Informatics, 19(9), 10082–10094.
43. Ahmed, M., Khan, S., & Gupta, R. (2023). Intelligent predictive maintenance framework using industrial IoT and edge analytics. Sensors, 24(5), 1728.
44. Silva, F., Costa, R., & Pereira, J. (2023). Computer vision applications for predictive maintenance in Industry 4.0 environments. Journal of Manufacturing Technology Management, 34(7), 1182–1204.
45. Sasi Kumar Kolla, Venkata Akhilesh Ranga Reddy. (2023). Deep Learning Architectures For Multimodal Medical Data Integration. South Eastern European Journal of Public Health, 248–260. https://doi.org/10.70135/seejph.vi.7132
46. Meda, R. (2020). Real-Time Data Pipelines for Demand Forecasting in Retail Paint Distribution Networks. Global Research Development (GRD) ISSN, 2455-5703.
47. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
48. Kim, J., Lee, H., & Park, S. (2023). Edge-based video analytics for real-time equipment health monitoring in smart manufacturing. IEEE Access, 12, 43782–43795.
49. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
50. Singh, V., Chandra, A., & Verma, P. (2023). Industrial computer vision and predictive analytics for resilient manufacturing systems. Computers & Industrial Engineering, 187, 109784.
51. Bandi, V. D. V. K. (2023). MLOps Frameworks for Reliable Model Deployment in Cloud Data Platforms.
52. Zhao, L., Huang, J., & Li, X. (2023). Remaining useful life prediction using deep learning and edge computing in industrial assets. Reliability Engineering & System Safety, 236, 109278.
53. Rajesh Mattaparthi. (2023). Deep Learning-Driven Combustion Anomaly Detection in Diesel Powertrains: A Multi-Sensor Fusion Approach for Real-Time ECM Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 1084 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8272
54. Rahman, M. M., Hasan, M. K., & Islam, S. (2023). AI-powered predictive maintenance for smart factories: A systematic review. IEEE Access, 12, 61542–61567.
55. Meda, R. End-to-End Data Engineering for Demand Forecasting in Retail Manufacturing Ecosystems.
56. Hossan, M. Z., Rahman, M. A., & Karim, R. (2023). AI for predictive maintenance in smart manufacturing. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 17(1), 1–15.
57. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
58. Yang, C., Xu, H., & Wu, J. (2023). Digital twin-driven predictive maintenance and asset resilience in smart manufacturing. Robotics and Computer-Integrated Manufacturing, 82, 102534.
59. Civerchia, F., Bocchino, S., Salvadori, C., Rossi, E., Maggiani, L., & Petracca, M. (2023). Industrial IoT monitoring and predictive maintenance architecture for resilient manufacturing ecosystems. Sensors, 23(14), 6318.
60. Bandi, V. D. V. K. Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics.
61. Alshammari, F., Alotaibi, B., & Alghamdi, M. (2023). Edge computing and machine learning integration for industrial asset health management. Future Generation Computer Systems, 152, 101–114.
62. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
63. Ferreira, P., Oliveira, M., & Santos, T. (2023). Asset resilience through AI-enabled monitoring and predictive analytics in Industry 4.0. Sustainability, 15(18), 13722.
64. Wu, Z., Chen, M., & Li, P. (2023). Vision transformers for predictive maintenance and anomaly detection in manufacturing environments. Engineering Applications of Artificial Intelligence, 128, 107533.
65. Reddy, V. A. R. (2023). API-First Design As A Strategy For Healthcare System Interoperability. South Eastern European Journal of Public Health, 224–247. https://doi.org/10.70135/seejph.vi.7128
66. Hamasha, M. M., Albedoor, Q., Hamasha, S., Ali, H., Qamar, A., & Berrah, F. (2023). A comprehensive framework for IoT-driven predictive maintenance: Leveraging AI and edge computing for enhanced equipment reliability. Journal of Applied Engineering Science, 23(3), 471–486.
