Development of DNN-based diagnostic platform for self-diagnosis of electric vehicle automatic transmission controller production equipment

Recently, with the remarkable development of artificial intelligence technology, a technique for monitoring and predictive maintenance (PdM) of industrial equipment performance based on production data is attracting attention. PdM is emerging as the most effective solution in terms of equipment defect diagnosis and remaining life evaluation in smart manufacturing and industrial big data platforms. In this paper, we predict in advance the cause of poor performance in production equipment that occurs during mass production of SCU (Shift-by-wire Control Unit) controller, a type of automatic transmission applied to electric vehicles. Finally, by applying data-based PdM, we propose an AI (Artificial Intelligence)- based self-diagnosis system for production equipment that enables workers to respond quickly before malfunction occur.