Operationalization of automated fault description mappings in quality management systems using the natural language processing neural networks of the BERT language model

Abstract

The automotive industry has witnessed remarkable technological advancements, leading to innovative approaches in quality management and fault categorization. This thesis focuses on improving the fault categorization process within Mercedes-Benz Operations 360 (MO360) platform by leveraging state-of-the-art transformer models like Bidirectional Encoder Representations from Transformers (BERT) and General Text Embedding (GTE) Large. Through advanced Natural Language Processing (NLP) techniques, the study addresses the challenges of mapping textual feedback to structured fault codes which is a crucial need in automotive production and customer service. The research begins with a thorough exploration of existing literature, highlighting the role of pre-trained language models in customer feedback analysis, fault diagnosis, and semantic text matching. Fine-tuned models are then developed and evaluated, achieving a training accuracy of more than 98%. Their performance is assessed using various metrics, and expert validation gives useful information about the system’s usability, accuracy, and usefulness in real life. These evaluations highlighted the system’s ability to simplify fault categorization while identifying areas for refinement, such as managing rare fault scenarios and ambiguous descriptions. An additional validation process, involving expert-labeled data, further enhanced the models’ accuracy, achieving a top-5 accuracy of 54% and a top-1 accuracy of 30.54%. Looking ahead, the thesis proposes future improvements, including multilingual support, real-time fault categorization, and explainable Artificial Intelligence (AI) features to increase transparency and trust. These findings demonstrate how NLP can significantly enhance quality management in the automotive sector, offering a robust and scalable framework that meets the industry’s evolving needs for precision and customer-focused solutions.