ISSN   1004-0595

CN  62-1224/O4

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张永芳, 侯丕鸿, 杨鑫亮, 陈锐搏, 康建雄, 邢志国, 吕延军. 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[J]. 摩擦学学报(中英文), 2025, 45(2): 1−10. doi: 10.16078/j.tribology.2024002
引用本文: 张永芳, 侯丕鸿, 杨鑫亮, 陈锐搏, 康建雄, 邢志国, 吕延军. 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[J]. 摩擦学学报(中英文), 2025, 45(2): 1−10. doi: 10.16078/j.tribology.2024002
ZHANG Yongfang, HOU Pihong, YANG Xinliang, CHENG Ruibo, KANG Jianxiong, XING Zhiguo, LÜ Yanjun. A Prediction Method of Surface Wear in Internal Combustion Engine Cylinder Liners Using Multi-Scale Combined Recurrent Neural Networks[J]. Tribology, 2025, 45(2): 1−10. doi: 10.16078/j.tribology.2024002
Citation: ZHANG Yongfang, HOU Pihong, YANG Xinliang, CHENG Ruibo, KANG Jianxiong, XING Zhiguo, LÜ Yanjun. A Prediction Method of Surface Wear in Internal Combustion Engine Cylinder Liners Using Multi-Scale Combined Recurrent Neural Networks[J]. Tribology, 2025, 45(2): 1−10. doi: 10.16078/j.tribology.2024002

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

  • 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.

     

    Abstract: 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 architectures addresses traditional RNNs’ limitations, such as difficulty in learning long-range data dependencies. This synthesis enhances the model’s predictive accuracy and reliability. Wear in the real world typically follows nonlinear patterns and is influenced by various factors such as constantly changing operating conditions, material fatigue, and environmental influences. Nonlinear models may better capture these complexities than linear models. A nonlinear transformation of the actual operating data from the RTA38 internal combustion engine cylinder liner is presented, wherein the total amount of wear is converted into an increment of wear. The transformation of this data proves beneficial for simulating more realistic changes in wear under actual working conditions. It is shown that these data are relevant for model training and testing across different indicators, training data ratios, and lengths of time series. The effectiveness of the model component is further validated by the ablation experiment. The superior performance of the MIXRNN over traditional RNNs and their variants is demonstrated across key performance indicators, including mean absolute error, mean relative error, root mean square error, and coefficient of determination. The robustness and predictive accuracy of the MIXRNN, especially in tests involving small sample datasets and extended time series data, are particularly notable. In conclusion, the MIXRNN is represented as a robust and accurate tool for the prediction of wear on internal combustion engine cylinder liners. Its capabilities contribute to the improvement of the scientific and rational aspects of the overall design and maintenance strategies for these engines.

     

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