基于多尺度-组合式循环神经网络的内燃机缸套表面磨损量预测方法  

A Prediction Method of Surface Wear in Internal Combustion Engine Cylinder Liners Using Multi-Scale Combined Recurrent Neural Networks

作　　者：张永芳 侯丕鸿 杨鑫亮 陈锐搏 康建雄 邢志国 吕延军[1] ZHANG Yongfang;HOU Pihong;YANG Xinliang;CHEN Ruibo;KANG Jianxiong;XING Zhiguo;LÜYanjun(School of Mechanical and Precision Instrument Engineering,Xi’an University of Technology,Shaanxi Xi’an 710048,China;School of Printing,Packaging Engineering and Digital Media Technology,Xi’an University of Technology,Shanxi Xi’an 710054,China;School of Mechanical and Electrical Engineering,Lanzhou University of Technology,Gansu Lanzhou 730050,China;National Key Lab for Remanufacturing,Academy of Armored Forces Engineering,Beijing 100072,China)

机构地区：[1]西安理工大学机械与精密仪器工程学院,陕西西安710048 [2]西安理工大学印刷包装与数字媒体学院,陕西西安710054 [3]兰州理工大学机电工程学院,甘肃兰州730050 [4]陆军装甲兵学院装备再制造技术国防科技重点实验室,北京100072

出　　处：《摩擦学学报(中英文)》2025年第2期315-324,共10页Tribology

基　　金：国家自然科学基金项目(52075438);陕西省重点研发计划项目(2024GX-YBXM-268);教育部数控机床及机械制造装备集成重点实验室开放基金项目(SKJC-2022-01)资助.

摘　　要：缸套作为内燃机的关键部件,其磨损状况直接影响内燃机活塞-缸套系统的服役性能.为了准确预测内燃机缸套表面的磨损状况,本文中提出了1种多尺度组合式的循环神经网络(MIXRNN)模型,该模型融合了多尺度特征提取技术与组合式循环神经网络(RNN)架构,通过捕捉和学习缸套磨损过程中的时序特征及其动态关系,使其具备了非线性磨损量回归的能力.基于内燃机缸套实际运行数据的测试表明:该模型在平均绝对误差、平均相对误差、根均方误差及决定系数等性能指标上显著优于传统的RNN及其变体模型,尤其在处理小样本数据集和长时间序列数据时,具有很好的鲁棒性和准确性,为内燃机活塞-缸套系统的剩余寿命预测和服役性能评估提供了参考和依据.In the paper,wear on the surface of internal combustion engine cylinder liners is the focus for prediction.The wear condition of liners significantly affects the sealing performance between the piston and liner,directly affecting the operational efficiency,reliability,and service life of internal combustion engines.With the ongoing evolution of sensor technology and data acquisition systems,the monitoring capabilities of mechanical systems have been greatly enhanced.Parameters such as temperature,pressure,and vibration are continuously tracked,allowing for real-time insights into the system’s condition to be obtained.This real-time monitoring serves as the foundation for predictive maintenance,in which maintenance activities are scheduled based on the actual state of the equipment,effectively preventing failures and minimizing operational downtime.In recent years,machine learning,a subset of artificial intelligence,has seen rapid growth and has found applications across various domains,including mechanical engineering.Machine learning algorithms’ability to process and analyze extensive datasets makes them valuable tools for predictive maintenance in the context of internal combustion engine cylinder liner wear prediction.In our study,a novel multiscale mixed Recurrent Neural Network(MIXRNN)model is introduced,characterized by the integration of multi-scale feature extraction techniques with a composite recurrent neural network(RNN)architecture,thereby enabling the adept capture and learning of the temporal characteristics and dynamics of wear on cylinder liners.The multi-scale feature extraction,a critical aspect of the MIXRNN model,is emphasized for its allowance of data analysis at varying scales or resolutions.This capability is deemed vital for discerning both short-term and long-term wear patterns in cylinder liners,which is essential for comprehending the multifaceted nature of mechanical wear that may emerge from diverse factors over varying time frames.Furthermore,the MIXRNN integration of various RNN

关 键 词：内燃机缸套 磨损量预测 多尺度特征 组合式循环神经网络 小样本训练 

分 类 号：TH117.2[机械工程—机械设计及理论]