计算机技术与发展2025，Vol.35Issue(3)：165-171,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0350
基于特征融合的机械核心部件剩余寿命预测
RUL Prediction of Mechanical Core Components Based on Feature Fusion
摘要
Abstract
Predictive maintenance of engineering machinery equipment can ensure the efficient operation of the equipment and the rational arrangement of maintenance plans,with the key lying in accurately predicting the RUL of equipment systems or core components.To address the challenges of feature extraction and prediction accuracy in predictive maintenance of engineering machinery equipment,we propose an Informer-based framework for the prediction of the RUL of core components of mechanical equipment based on feature fusion.Firstly,an optimized data utilization method is adopted to construct training samples according to the proportion of life.Bartlett high-pass filter and wavelet denoising are used to filter and denoise the raw data,and feature extension is performed to extract key features from the raw data.The XGBoost algorithm is used for feature selection.Then,the selected data is classified by equipment type,and an Informer-based predictive model for the RUL of mechanical core components is designed for classification training.The model is validated using public datasets,demonstrating that the Informer predictive model based on feature fusion achieves the highest prediction accuracy compared to other models.关键词
预测性维护/剩余寿命预测/特征提取/特征融合/深度学习/自注意力机制Key words
predictive maintenance/RUL/feature extraction/feature fusion/deep learning/self-attention mechanism分类
计算机与自动化引用本文复制引用
刘宗宇,廖雪超..基于特征融合的机械核心部件剩余寿命预测[J].计算机技术与发展,2025,35(3):165-171,7.基金项目
国家自然科学基金项目(62273264) （62273264）
