基于神经网络的气囊隔振装置对中状态评估方法  

Neural network-based evaluation method for alignment state of air spring vibration isolation device

作　　者：刘志伟 施亮[1,2] 刘松 LIU Zhiwei;SHI Liang;LIU Song(Institute of Noise and Vibration,Naval University of Engineering,Wuhan 430033,China;National Key Laboratory on Ship Vibration and Noise,Wuhan 430033,China)

机构地区：[1]海军工程大学振动与噪声研究所,湖北武汉430033 [2]船舶振动噪声重点实验室,湖北武汉430033

出　　处：《中国舰船研究》2024年第6期117-125,共9页Chinese Journal of Ship Research

基　　金：重点实验室基金资助项目(6142204220104)。

摘　　要：[目的]针对现有轴系对中状态监测模型难以准确描述非线性、时变条件下对中状态的难题,提出一种基于神经网络模型的对中状态评估方法。[方法]首先,建立基于BP神经网络的对中状态预测模型,制定典型工况下的训练与测试数据获取方式,对数据进行移动平均降噪处理,并总结模型超参数的调整规律;然后,分别在小型及大型气囊隔振装置上开展试验研究。[结果]结果显示,建立的神经网络模型仅通过气囊压力数据,即可准确预测隔振装置的对中状态,且在不同型号的装置间有较强的通用性,预测误差小于0.5,对中预测准确度可达96.29%。[结论]所建立的模型不依赖系统参数,在小型和大型装置的对中状态预测中均表现良好,可为动力设备启动后的动态对中状态预测及轴系对中控制提供理论支撑.[Objectives]To address the difficulty in accurately describing the states of alignment under nonlinear and time-varying conditions in existing monitoring models,a neural network-based method for evaluating the states of alignment is proposed.[ Method] A BP neural network-based prediction model is developed.The typical working conditions for acquiring training and testing data are defined,and the data is denoised using a moving average filter.The rules for adjusting the model's hyperparameters are summarized.Experimental studies are then carried out on both small and large air spring isolation devices.[Results]The results demonstrate that the neural network model can accurately predict the states of isolation devices alignment using only the air spring pressure data.The model exhibits strong generalizability across different device types,with a prediction error of less than 0.5 and an alignment prediction accuracy of 96.29%.[Conclusion]The proposed model does not rely on system parameters and performs well in predicting the states of alignment for both small and large devices.The results of this study can provide theoretical support for the state prediction of alignment in a dynamic way and shaft alignment control of power equipment after startup.

关 键 词：轴系 对中 气囊隔振装置 神经网络 PyTorch 

分 类 号：U664.21[交通运输工程—船舶及航道工程]