Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 516-521· 0 citations· 15 references
Abstract
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines. The system uses an Arduino Uno (ATmega328P) microcontroller that is connected to several sensors, such as voltage, current, temperature (LM35), vibration, proximity, and MPU6050 sensors, to collect important operational data. The ESP8266 NodeMCU Wi-Fi module sends the processed data to an IoT cloud platform so that it can be monitored and analyzed from afar. There is also an I2C display developed in for real-time viewing on position. The proposed system uses AI-based analysis to find problems and assume when equipment might break down, which enables maintenance be performed on time. By using a hardware prototype to evaluate shows that the system reliably monitor in real time and make accurate predictions, which cuts down on downtime and makes the system work better. The Integration of AI and IoT technologies makes predictive maintenance in modern industrial settings cheaper, more flexible, and smarter.
The fast changing nature of connected vehicles requires smart systems that can monitor health in real-time and predict failures. This paper introduces an IoT-based Smart Vehicle Monitoring and Predictive Maintenance System that involves onboard sensors, edge computing, and cloud analytics to ensure high reliability and...
Biswaraj Roy, B. Prasath, Ajit Kumar Singh et al.· 2026 6th International Confe...· 0 citations
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industr...
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
Industrial machinery operating in manufacturing and process environments is frequently subjected to adverse operating conditions such as excessive temperature rise, abnormal current consumption, and mechanical vibrations, which may lead to performance degradation, unexpected failures, production losses, and safety haza...
Sudharshana, Kratika V Ulman, Kishan K Kulal et al.· 2026 International Conferenc...· 0 citations
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Govind D. More, Shreyas Hon, Piyush Kotkar et al.· International Journal of Cre...· 0 citations
Experimental results have shown that the system operates stably with supply voltage in the range of 12.18-12.35 V, near-real-time cloud synchronization, and average prediction time of 145 ms, proving the feasibility of the proposed system design.
Chellan P., D. M, Sivasubramani. G. et al.· Journal of Electrical Engine...· 1 citation
Experimental validation demonstrates reliable phase detection, rapid relay response, and effective remote alerting, confirming the system's suitability for industrial automation, motor protection, and smart energy management applications.
J. Babu, M. Divya, Varu Chirag et al.· International Journal for Sc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
