Artificial intelligence system for enhancing product’s performance during its life cycle in a railcar industry

Abstract

The management of the activities which characterize the lifecycle of a product could be challenging, hence, a well-structured Product Lifecycle Management System (PLMS) with Artificial Intelligence (AI) capability can offer a sustainable solution in this regard. This will promote data driven maintenance because AI and data analysis can drive the activities of the entire product’s lifecycle. The aim of this study is to demonstrate the use of AI for enhancing products’ performance during its use phase in the products’ life cycle and to develop a framework for the proposed PLMS and AI capability. The bearing component of a railcar was used as a case study. The temperature data of the bearing component employed were obtained from primary and secondary sources and were pre-processed in order to extract features and indicators for the training and predictive model development. The acquisition of the input data was followed by data pre-processing to remove noise and iterative training to obtain the predictive model. The training was done using the Levenberg Marquardt algorithm in a MATLAB 2018a environment in order to predict future temperature variations and the remaining useful life of the bearing. The results obtained indicated that the AI is suitable for condition based monitoring and prediction of the time to failure as well as the Remaining Useful Life (RUL) of the railcar bearing. It is envisaged that with the AI capability integrated into the PLMS will enhance components performance through effective monitoring during its use phase.