The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems.

To address the lack of compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) in conventional timed elastic band (TEB)—based dynamic path planning for autonomous surface vessels (ASVs), this study proposes an intelligent path-planning framework that integrates an improved jump point search (JPS) algorithm with a COLREGs-aware TEB approach. At the global planning layer, an enhanced JPS algorithm incorporating redundant-node elimination and cubic Bezier curve smoothing is developed to construct the JPS–PB method, thereby reducing path redundancy and improving trajectory continuity. At the local planning layer, COLREGs encounter rules are reformulated as differentiable cost functions and embedded into the TEB optimization framework, enabling rule-compliant collision avoidance in typical maritime encounter scenarios, including head-on, crossing, and overtaking situations. Simulation results demonstrate that, compared with the conventional JPS algorithm, the proposed JPS-PB method reduces the path length by up to 8.6%, decreases the cumulative steering angle by 58.6%, and lowers the maximum steering angle by 88.6% on a 12 × 12 grid environment. Furthermore, compared with the conventional TEB algorithm, the proposed COLREGs–aware TEB (CTEB) increases the Distance at the Closest Point of Approach (DCPA) by 543%, 20.5%, 4.8%, and 13.8% in starboard-crossing encounters, head-on, overtaking, and special port-side-crossing encounter scenarios, respectively. Parameter sensitivity analyses further indicate that CTEB maintains stable collision-avoidance performance and rule-compliance capability under variations in the danger–distance threshold, danger–time threshold, and COLREGs cost weight. The results verify that the proposed JPS–PB+CTEB hybrid framework effectively improves path quality, dynamic obstacle-avoidance safety, and navigation-rule compliance for autonomous ASV operations.

Distributed model predictive control (MPC) coordinates constrained multi-agent systems while preserving computational locality, yet an implementation can cease to represent its stated convex program. We study a minimal linear plant so communication denial is a modeled source of uncertainty, and treat centralized equivalence as a hard gate: an edge-splitting consensus alternating direction method of multipliers (ADMM) formulation of the formation-tracking quadratic program is built so every distributed update can be audited against a centralized solver. Against a Lagrangian dual-decomposition baseline, ADMM attains the same accuracy without step-size tuning. Across topologies, horizons, penalties, budgets, and denial windows, the exact branch matches the centralized optimum to numerical precision, the equivalence gate and saturation signature survive scaling to eight agents, and the saturation signature also survives measurement noise. For communication denial, we prove a bounded-error result separating the summable-error regime, which recovers the optimum exactly, from the merely-bounded regime, whose limit points remain O(ε)-optimal. Coupling each edge weight to message age through an exponentially decaying confidence factor contracts the distributed problem to uncoupled local MPC, so formation error saturates as outages lengthen; a fixed-weight ablation isolates this decay as the causal mechanism. We contribute a reproducible baseline and two honest mechanisms—centralized-equivalence gating and confidence-decayed coupling—for distributed formation MPC.