Predictive maintenance has become an important factor in improving the reliability and efficiency of industrial robots in the evolving environment of smart manufacturing. The proposed paper is a predictive maintenance framework based on AI to be implemented to multi-sensor industrial robots that will be used in a smart manufacturing setting. The point is to be able to develop a model that will combine multiple sensor data (e.g., temperature, vibration, force, and acoustic signals) with sophisticated machine learning models to anticipate possible problems in robotic systems. Early warning of mechanical failures can be achieved through sensor fusion and AI methods, enabling the framework to identify problems in the machine at an early stage and implement corrective measures in time to reduce downtime. The deep learning model was a hybrid between a convolutional neural network (CNNs) and a long short-term memory (LSTM) network, where time-series sensor data was processed and equipment malfunctions predicted. The model was trained and tested on a real-world dataset (smart factory), which is sensor readings of industrial robots. The findings indicate that the method has got an accuracy rate of 92.5% in failure prediction and is better than the traditional methods in accuracy and recall. Moreover, the system provides real-time health information for the robot, greatly reducing the cost and time required for unscheduled maintenance. The paper will end with a discussion of the implications of using AI to integrate predictive maintenance in smart manufacturing and define future directions of the model in the context of various industrial configurations in order to increase its scale and applicability.
Gokhale K. AI-Driven Predictive Maintenance and Energy-Efficient Robotics for Adaptive Production Systems. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management. 2025;(03):26–31.
2.
Hasnawi AA, Abdul-Rahaim LA, Muwafaq Gheni H, Emad Fadel Z. IoT Structure based Supervisor and Enquired the Greenhouse Parameters. Journal of Internet Services and Information Security. 2024;14(1):138–52.
3.
Dey S, Sharma P. Predictive Maintenance for Smart Manufacturing: An AI and IoT-Based Approach. Library of Progress-Library Science. 2024;(3).
4.
Leema AA, Balakrishnan DrP, Jothiaruna N. Harnessing the Power of Web Scraping and Machine Learning to Uncover Customer Empathy from Online Reviews. Indian Journal of Information Sources and Services. 2024;14(3):52–63.
5.
Khatun Z. AI-DRIVEN PREDICTIVE MAINTENANCE FOR MOTOR DRIVES IN SMART MANUFACTURING A SCADA-TO-EDGE DEPLOYMENT STUDY. American Journal of Interdisciplinary Studies. 2025;06(01):394–444.
6.
Mohammed Hussain V, Abdul Azeez Khan DrA, Sathick DrJ, Raj DrA, Haja Alaudeen DrA. Machine Learning Based Vehicle Traffic Patterns Prediction Model (ML-VTPM) With Mobile Crowd Sensing for Transportation System. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2025;16(1):1–25.
7.
Huang Z, Shen Y, Li J, Fey M, Brecher C. A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors. 2021;21(19):6340.
8.
Wang X, Liu M, Liu C, Ling L, Zhang X. Data-driven and Knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing. Expert Systems with Applications. 2023;234:121136.
9.
Haque R, Bajwa A, Siddiqui NA, et al. PREDICTIVE MAINTENANCE IN INDUSTRIAL AUTOMATION: A SYSTEMATIC REVIEW OF IOT SENSOR TECHNOLOGIES AND AI ALGORITHMS. American Journal of Interdisciplinary Studies. 2024;5(1):01–30.
10.
Liu Y, Yu W, Dillon T, Rahayu W, Li M. Empowering IoT Predictive Maintenance Solutions With AI: A Distributed System for Manufacturing Plant-Wide Monitoring. IEEE Transactions on Industrial Informatics. 2022;18(2):1345–54.
11.
Pookkuttath S, Rajesh Elara M, Sivanantham V, Ramalingam B. AI-Enabled Predictive Maintenance Framework for Autonomous Mobile Cleaning Robots. Sensors. 2021;22(1):13.
12.
Lee J, Singh J, Azamfar M, Pandhare V. Industrial AI and predictive analytics for smart manufacturing systems. Smart Manufacturing. Elsevier; 2020. p. 213–44.
13.
Azeta J, Omeche TT, Daniyan I, Abiola JO, Daniyan L, Phuluwa HS, et al. Artificial intelligence and robotics in predictive maintenance: a comprehensive review. Frontiers in Mechanical Engineering. 2026;11.
14.
Yao X, Yan H, Zhou J, Li Y, Yu H. Smart Manufacturing, Robotics, and AI Systems. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering. Wiley; 2025. p. 61–78.
15.
Dineshkumar P. AI-Based Predictive Maintenance in Industrial Robotics. SECITS Journal of Scalable Distributed Computing and Pipeline Automation. 2024;(1):32–8.
16.
Okpala C, Chikwendu U, Onyeka N. Artificial intelligence-driven total productive maintenance: The future of maintenance in smart factories. International Journal of Engineering Research and Development. 2025;(1):68–74.
17.
Çınar ZM, Abdussalam Nuhu A, Zeeshan Q, Korhan O, Asmael M, Safaei B. Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0. Sustainability. 2020;12(19):8211.
18.
Dhinakaran D, Edwin Raja S, Velselvi R, Purushotham N. Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications. SN Computer Science. 2025;6(2).
19.
Shamim MMR. AI-DRIVEN PREDICTIVE MAINTENANCE FOR HIGH-VOLTAGE X-RAY CT TUBES: A MANUFACTURING PERSPECTIVE. Review of Applied Science and Technology. 2024;03(01):40–67.
20.
Bitam T, Yahiaoui A, Boubiche DE, Martínez-Peláez R, Toral-Cruz H, Velarde-Alvarado P. Artificial Intelligence of Things for Next-Generation Predictive Maintenance. Sensors. 2025;25(24):7636.
21.
Pech M, Vrchota J, Bednář J. Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors. 2021;21(4):1470.
22.
Maguluri LP, Suganthi D, Dhote GM, Kapila D, Jadhav MM, Neelima S. AI-enhanced predictive maintenance in hybrid roll-to-roll manufacturing integrating multi-sensor data and self-supervised learning. The International Journal of Advanced Manufacturing Technology. 2024;
23.
Ayeni O. Integration of Artificial Intelligence in predictive maintenance for mechanical and industrial engineering. International Research Journal of Modernization in Engineering Technology and Science. 2025;(3):1–23.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
