A Hybrid IoT-Based Framework for Real-Time Parameter Estimation and Predictive Maintenance in Hydraulic Excavators

Accurate simulation models are essential for excavator stability control, particularly when addressing complex interactions across subsystems such as the engine and hydraulic system. The Simulink-based model used in this research incorporates a complete excavator system, including engine, hydraulic, and mechanical dynamics, requiring precise parameter estimation (PE) to handle nonlinear behaviors and unmeasurable parameters like friction coefficients and initial pressures. Traditional PE methods often struggle with these challenges, leading to inaccuracies in dynamic modeling. To address this, a novel hybrid PE framework is introduced, integrating machine learning (ML) techniques such as random forest (RF) and gradient boosting (GB) with optimization methods like gradient descent (GD) and nonlinear least squares (NLS). This framework bridges physicsbased simulations and real-time IoT-acquired data, enabling accurate estimation and correction of parameters. Validated using CAN bus-compatible datasets, the proposed framework achieves robust predictive accuracy, with a root mean square error (RMSE) of 0.3921, mean absolute error (MAE) of 0.0557, a coefficient of determination (R2) of 0.9999, and a symmetric mean absolute percentage error (SMAPE) of 0.84%. These results highlight the models suitability for high-fidelity applications in dynamic environments. Unlike conventional methods, the hybrid model consistently aligns with simulated outputs and real-world dynamics, with scalable performance across various operating conditions. It offers red compelling potential for real-time predictive maintenance, operational optimization, and integration with digital twin technologies in industrial IoT systems.