Remaining Useful Life Prediction Method for Highway Electromechanical Equipment Based on Bayesian Algorithm-Optimized CNN-LSTM Model

<div class="section abstract"><div class="htmlview paragraph">Efficient maintenance of highway electromechanical equipment is crucial for ensuring reliability within intelligent highway infrastructure and optimizing the allocation of limited maintenance resources. Traditional Remaining Useful Life (RUL) prediction models frequently face limitations due to the complex and dynamic operating conditions of such systems, which often hinder their predictive accuracy and adaptability. To overcome these persistent challenges, this study introduces an advanced RUL prediction model that integrates a Bayesian-optimized Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. Initially, the study identifies key health indicators that effectively represent the degradation of equipment performance over time. These indicators undergo Spearman correlation analysis to determine their relevance to equipment capacity, ensuring that only the most pertinent features are used for model input. The CNN-LSTM model leverages CNN’s spatial pattern recognition and LSTM’s ability to process temporal sequences, allowing it to accurately capture performance trends over time and improve long-term reliability. To further enhance model accuracy, Bayesian optimization is applied to adjust the model’s hyperparameters automatically, providing an efficient, tailored solution that aligns with the unique characteristics and operational demands of highway electromechanical equipment. Validation on the CALCE lithium battery dataset demonstrated a prediction accuracy exceeding 92%, confirming the model's feasibility, robustness, and strong potential for real-world application in highway system maintenance. The study provides valuable insights for optimizing the operation, management, and maintenance of highway electromechanical equipment. It supports predictive maintenance strategies that enhance maintenance scheduling efficiency, extend the lifespan of critical infrastructure, and effectively reduce operational costs while simultaneously improving overall system reliability.</div></div>