A process-guided uncertainty-aware deep learning framework for reliable and interpretable industrial fault diagnosis

Timely fault detection is essential for safety, product quality, and energy efficiency in advanced industrial processes. However, many existing fault diagnosis methods insufficiently exploit process structure and sensor reliability, which limits their robustness and practical usefulness for process engineers. This study presents an improved framework SAU-PGA-CNN-BiLSTM that first couples Convolutional Neural Networks and Bidirectional Long Short-Term Memory layers to extract multivariate temporal dynamics and spatial correlations of the process data, secondly a process guided and sensor-aware attention mechanism is introduced which embeds process centrality, sequence level sensor reliability and uncertainty to the attention learning, to suppress unreliable channels and bias towards informative and stable sensors. In addition, Monte Carlo dropout with sensor prior-conditioning is used to provide calibrated confidence estimates that reflect both predictive uncertainty and sensor reliability. Finally, two lightweight sigmoid output heads perform fault detection and diagnosis combinedly, promoting mutual reinforcement between the tasks. Validated on the Tennessee Eastman Process benchmark, the proposed framework outperforms baselines model and achieves 93.6% multiclass diagnosis accuracy with 94.0% F1 score. After temperature scaling, the proposed model also demonstrates improved calibration compared with an otherwise identical model without sensor awareness, reducing negative log-likelihood from 0.197 to 0.182, Brier score from 0.101 to 0.095, and expected calibration error from 0.040 to 0.037. Attention visualizations further show that the model focuses on process-relevant and reliable sensors, supporting reliable industrial fault diagnosis.