Daniyan, IlesanmiMpofu, KhumbulaniMuvunzi, RumbidzaiUchegbu, Ikenna Damian2025-02-142025-02-142022-01-012212-8271 (E)http://dx.doi.org/10.1016/j.procir.2022.05.277https://hdl.handle.net/20.500.14519/135932nd CIRP Design 2022 (CIRP Design 2022).The quest to enhance maintenance operation in the rail industry has continued to occupy the front burner in recent time in a bid to reduce machine’s downtime and ensure smooth operations. In this study, Artificial Intelligence (AI) technique was proposed for maintenance operation of wheel-bearing component of a railcar. Data from a secondary source was pre-processed and iteratively trained using specialised training algorithm in a machine learning environment under a supervised training until it produces a model capable of making predictions. The result obtained indicates the feasibility of the developed AI model for prediction of the Remaining Useful Life of a wheel-bearing component. The result shows that the wheel-bearing will last for 500 hours over the next 40 days before it begins to fail in service. The wheel bearing starts showing sign of degradation on day 41 of usage. Upon the use of the predictive model, the predicted RUL, confidence bound and slope detection instant were obtained. Hence, the implementation of AI for predictive maintenance could promote maintenance operation in the rail industry.449-453 PagesenAttribution-NonCommercial-ShareAlike 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-sa/4.0/AIMachine learningMaintenance operationRULWheel-bearingPresentation