Time-to-Fault Prediction Framework for Automated Manufacturing in Humanoid Robotics Using Deep Learning

Abstract

Industry 4.0 is transforming predictive failure management by utilizing deep learning to enhance maintenance strategies and automate production processes. Traditional methods often fail to predict failures in time. This research addresses this issue by developing a time-to-fault prediction framework that utilizes an enhanced long short-term memory (LSTM) model to predict machine faults. The proposed method integrates real-time sensor data, including current, voltage, and temperature calibrated via ultra-sensitive optical sensing technologies based on the typical whispering gallery optical mode (WGM) to create a robust dataset. Due to the high-quality factor that these sensors exhibit, any minute change on the surrounding medium will makes a significant change on its transmission spectrum. The LSTM model trained on these data demonstrated rapid and stable convergence, outperforming other deep learning techniques with a mean absolute error (MAE) of 0.83, a root mean squared error (RMSE) of 1.62, and a coefficient of determination (R2) of 0.99. The results show the superior performance of LSTM in predicting machine failures early in real-world environments within 10 min lead time, improving productivity and reducing downtime. This framework advances smart industries by improving fault prediction in manufacturing precision robotics components, demonstrated through two humanoid robots, GUCnoid 1.0 and ARAtronica.