Author:
Shivani Magdum ,Dr.B.F.Momin
Abstract
In the field of artificial intelligence, ensuring timely maintenance of mechanical devices like bikes, cars, air conditioners, etc., is crucial. This research paper proposes the design of a user-friendly Mobile Application that seamlessly connects with Calibration devices and utilizes advanced algorithms to predict system failure dates, assess device health, and provide proactive service and failure information. The application offers proactive service recommendations and alerts by analysing data from pressure controllers and considering factors such as calibration, aging, subsystem failures, and component failures. It optimizes maintenance schedules and minimizes downtime through state-of-the-art predictive maintenance algorithms. This research aims to significantly enhance the reliability and efficiency of mechanical devices by accurately predicting issues and providing preventive measures.
Reference8 articles.
1. Singh, A., Kumar, V. (2018). Predictive Maintenance: State of the Art and Research Challenges. Journal of Mechanical Engineering Research and Developments, 41(2), 79-88.
2. Li, X., Liu, J., and Chen, J. (2020). Predictive Maintenance for Industrial Systems: Methods and Applications. IEEE Transactions on Industrial Informatics, 16(12), 7471-7480.
3. Rao, B., Rao, R., and Venkatachalam, G. (2019). A Review of Predictive Maintenance Techniques for Electro-Mechanical Systems. Journal of Computational and Theoretical Nanoscience, 16(2), 853-860.
4. Khoshnoudian, F., and Yau, D. K. Y. (2018). An Intelligent Predictive Maintenance Framework for Industrial Control Systems. IEEE Transactions on Industrial Informatics, 14(11), 5097-5106.
5. Y. Wang and Y. Wang, "Using social media mining technology to assist in price prediction of the stock market," 2016 IEEE International Conference on Big Data Analysis (ICBDA), Hangzhou, China, 2016, pp. 1-4, doi: 10.1109/ICBDA.2016.7509794.