Integrating BERT with Recurrent Neural Networks for Predictive Analytics in Product Safety and Reliability Engineering

In the domain of product safety and reliability engineering, predictive analytics plays a crucial role in identifying potential failures and ensuring high-quality manufacturing standards. This study explores the integration of Bidirectional Encoder Representations from Transformers (BERT) with Recurrent Neural Networks (RNNs) to enhance predictive capabilities in assessing product reliability. BERT, renowned for its powerful natural language processing (NLP) capabilities, is employed to extract meaningful features from textual data, such as engineering documentation and failure logs. These features are then combined with sequential data processed by RNNs, which excel in capturing temporal dependencies and patterns in time-series data. The combined approach leverages BERT’s contextual understanding to improve feature representation and RNN’s strength in modeling sequences to predict internal failures more accurately. The Bosch Production Line Performance dataset serves as the foundation for this research, providing extensive measurements and test results from various stages of the assembly line. The integration of BERT and RNNs allows for a comprehensive analysis of both textual and numerical data, enabling the prediction of component failures and optimization of manufacturing processes. This hybrid model achieves a predictive accuracy of 98.5% and delivers actionable insights to enhance product safety and reliability.By combining advanced NLP techniques with robust sequence modeling, this study offers a novel approach to tackling complex predictive challenges in product engineering, ultimately contributing to more reliable and cost-effective manufacturing practices.