This paper presents a deep learning-based adaptive sensor fusion framework for re-al-time control and fault-tolerant automation in Industrial IoT systems. The core of the framework is an attention-based CNN-Transformer model that dynamically fuses het-erogeneous sensor streams; its interpretable weighting signals are leveraged directly for fault detection and to inform a supervisory control policy. By dynamically weighting multiple heterogeneous sensor streams using an attention-based CNN-Transformer architecture, the proposed method reduces estimation error under noisy and fault-prone conditions, and seamlessly integrates with a closed-loop controller that adjusts to detected faults through a stability-aware supervisory policy. Experiments on synthetic IIoT data with injected transient faults demonstrate significant improvements in fusion accuracy (RMSE: 0.049 ± 0.003 vs 0.118 ± 0.008 for Kalman filter, p < 0.001), faster fault detection (F1-score: 0.89 ± 0.02) and recovery (1.1 ± 0.2 seconds), and hard real-time performance suitable for edge deployment (99th percentile latency: 58ms). The results show that the proposed approach outperforms classical baselines in terms of RMSE, detection F1-score, recovery time, and latency trade-offs. This work contributes to more reliable, adaptive automation in industrial settings with minimal manual tuning and empirical stability validation.