Artificial intelligence for predictive maintenance in the railcar learning factories
Ilesanmi Daniyan, Khumbulani Mpofu, Moses Oyesola, Boitumelo Ramatsetse, Adefemi Adeodu
Abstract
The learning factories are platform created to provide an effective learning environment that will bring about human capacity development in a bid to bridge the gap between learning and practice. In this study, the training modules involving the Artificial Intelligence (AI) system, which comprises of the Artificial Neural Network (ANN) with dynamic time series model was developed. This is to train maintenance personnel on how to constantly monitor and analyze data from the Internet of Things (IoT) and other sources in order to predict the state and potential failure of a railcar wheel bearing. The modules for training include the data acquisition, pre-processing, network training, features extraction and predictive model modules which are set up to acquaint the personnel in the maintenance section of the rail industry on the use of AI for condition based monitoring and prediction of wheel bearing failure. The demonstration of the training modules was carried out using the past data of the wheel-bearing temperature from a secondary source which was pre-processed and iteratively trained using the Levenberg Marquardt algorithm in a MATLAB 2018a environment in order to predict future temperature variations, the remaining useful life of the bearing and to obtain a predictive model. The result obtained indicates the feasibility of the AI in the diagnosis of the railcar wheel bearing condition, prediction of the Remaining Useful Life (RUL) of the bearing as well as the determination of the optimum time for maintenance.