Integrated Diagnosis and Prognosis of Dynamic Systems Using Deep Learning: Case Study on a Welding Robot

This study addresses the challenges of diagnosis and prognosis in complex dynamic systems, focusing on an industrial MIG/MAG robotic welding application. A robust framework based on Recurrent Neural Networks (RNNs), specifically LSTM and GRU architectures, was developed to analyze multivariate time-series data from real-world sensor measurements for fault detection and remaining useful life (RUL) prediction. The approach incorporates a post-hoc attention mechanism to enhance interpretability by identifying the most influential variables and time windows contributing to each prediction. A sliding-window method (50 historical steps and a 10-step prediction horizon) was employed, and the models were evaluated against various benchmarks, including decision trees, feedforward neural networks, random forests, 1D CNNs, CNN–LSTM, and transformer architectures. Experimental results demonstrate that GRU and LSTM models outperform all baselines in both diagnostic and prognostic tasks, achieving up to 94% diagnostic accuracy and inference times of less than 100 ms, making them suitable for real-time deployment. The attention mechanism consistently identified key degradation signatures in more than 85% of fault sequences, offering valuable insights for domain experts. The proposed RNN-based framework therefore combines predictive accuracy, real-time performance, and interpretability, providing a scalable and effective solution for intelligent maintenance in industrial robotic systems.