Predictive Maintenance of Rotating Machinery using Machine Learning

We've We have developed a technique to process sensor data from various electrical motor pump sets fitted onboard ships, recorded by a network of sensors. Our application involves pre-processing techniques for cleaning, structuring and filtering data. EDA has allowed us to understand the dataset for its application for predictive maintenance. Further, we incorporated machine learning techniques and empirical rules to perform comparative analytics of data of similar machines [6, 9]. A complete application along with GUI was developed and deployed onboard a ship by combining pre-processing techniques, EDA, and comparative analysis module.

[1] J. Antonino-Daviu, Electrical monitoring under transient conditions: A new paradigm in electric motors predictive maintenance, Applied Sciences, 10 (2020).  [2] E. P. Carden and P. Fanning, Vibration based condition monitoring: a review, Structural health kmonitoring, 3 (2004), pp. 355–377. [3] J. Fausing Olesen and H. R. Shaker, Predictive maintenance for pump systems and thermal power plants: State-of-the-art review, trends and challenges, Sensors, 20 (2020). [4] A. N. Huda and S. Taib, Application of infrared thermography for predictive/preventive maintenance of thermal defect in electrical equipment, Applied Thermal Engineering, 61 (2013), pp. 220–227. [5] V. J. Jimenez, N. Bouhmala, and A. H. Gausdal, Developing a predictive maintenance model for vessel machinery, Journal of Ocean Engineering and Science, (2020). [6] R. Kakkar, J. Alzubi, A. Dua, S. Agrawal, S. Tanwar, R. Agrawal, G. Sharma, P. N. Bokoro, and R. Sharma, Padaav: blockchain-based parking price prediction scheme for sustainable traffic management, IEEE Access, 10 (2022), pp. 50125–50136. [7] S. Kaparthi and D. Bumblauskas, Designing predictive maintenance systems using decision tree-based machine learning techniques, International Journal of Quality & Reliability Management, (2020). [8] S. Keartland and T. L. Van Zyl, Automating predictive maintenance using oil analysis and machine learning, in 2020 International SAUPEC/RobMech/PRASA Conference, 2020, pp. 1–6. [9] R. Kothari, R. Kakkar, S. Agrawal, P. Oza, S. Tanwar, B. Jayaswal, R. Sharma, G. Sharma, and P. N. Bokoro, Selection of best machine learning model to predict delay in passenger airlines, IEEE Access, (2023), pp. 1–1. [10] C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, and R. E. Vásquez, Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis, Neurocomputing, 168 (2015), pp. 119–127.