Bearings are prone to wear, fatigue, and other issues during long-term operation due to mechanical stress, insufficient lubrication, and other factors. Accurate Remaining Useful Life (RUL) prediction is crucial for equipment maintenance. Among existing methods, statistical features struggle to capture complex nonlinear relationships, while end-to-end models suffer from poor interpretability. To address this, this paper proposes a Temporal Convolutional Network-Bidirectional Long Short-Term Memory (TCN-BiLSTM) prediction method that integrates Temporal Convolutional Network-Contrastive Predictive Coding (TCN-CPC) with statistical features. The approach extracts long-term dependencies through TCN and enhances feature representation using CPC. SHapley Additive exPlanations (SHAP) analysis reveals that statistical features contribute significantly during the healthy stage, while features extracted by deep learning exhibit stronger discriminative power in the failure stage. Building on this, the TCN-BiLSTM model further improves the accuracy of bearing condition prediction for RUL estimation. Experimental results demonstrate that the proposed TCN-BiLSTM achieves superior performance with average improvements of 43.2% in RMSE, 58.8% in MAE, and 55.1% in R ² compared to the strongest baseline BiLSTM-Attention. This validates the effectiveness of the feature fusion strategy.

Stable control of boiler drum water level is a core component in ensuring industrial production efficiency and operational safety. However, traditional fuzzy proportional-integral-derivative (PID) controllers suffer from deficiencies such as inadequate dynamic performance and weak interference resistance, making them ill-suited for complex operational conditions; while PID controllers optimized using conventional genetic algorithms (GA) can improve certain performance metrics, they often fall into the trap of ‘over-optimizing a single metric at the expense of overall performance balance’ (such as reducing regulation time while causing overshoot), and their convergence speed remains suboptimal. To address these issues, this paper proposes an Improved Genetic Algorithm (IGA)-optimized Fuzzy PID controller (IGA-Fuzzy PID), which integrates the advantages of Fuzzy Logic Control (FLC) and IGA to achieve synergistic optimization of the system’s overall performance. To validate its effectiveness, simulation experiments were conducted on the proposed IGA-Fuzzy PID controller, combined with Bode plot analysis to assess system stability, and compared with traditional Fuzzy PID and GA-optimized Fuzzy PID controllers. The results demonstrate that the IGA-Fuzzy PID controller exhibits significant advantages in optimizing overall system performance, achieving rapid disturbance response, and ensuring stable recovery.