基于LPSO-GRNN模型的螺栓松紧状态预测研究  

Prediction of tension state of bolt based on LPSO-GRNN

作　　者：梁伟[1,2,3] 陈志雄 欧阳忠杰 龚晟炜 钟建华 钟舜聪 廖华忠[3] LIANG Wei;CHEN Zhixiong;OU YANG Zhongjie;GONG Shengwei;ZHONG Jianhua;ZHONG Shuncong;LIAO Huazhong(College of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China;Fujian Provincial Key Laboratory of Force Measurement and Testing(Fujian Institute of Metrology),Fuzhou 350100,China;Xiamen Industrial Technology Research Institute,Xiamen 361001,China;Fujian Provincial Key Laboratory of Terahertz Functional Devices and Intelligent Sensing,Fuzhou 350108,China;School of Advanced Technology,Xi'an Jiaotong-Liverpool University,Suzhou 215123,China)

机构地区：[1]福州大学机械工程及自动化学院,福建福州350108 [2]福建省力值计量测试重点实验室(福建省计量科学研究院),福建福州350100 [3]厦门产业技术研究院,福建厦门361001 [4]福建省太赫兹功能器件与智能传感重点实验室,福建福州350108 [5]西交利物浦大学智能工程学院,江苏苏州215123

出　　处：《机电工程》2023年第11期1814-1822,共9页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(52205558);福建省自然科学基金资助项目(2021J01564);福建省教育厅中青年教师教育科研资助项目(JAT200022);福建省力值计量测试重点实验室开放课题基金资助项目(FJLZSYS202103)。

摘　　要：在轴重式动态汽车衡的服役状态下,由于受到重型货车频繁的加卸载循环冲击,会导致其内部螺栓发生松弛脱落,针对这一问题,提出了一种基于莱维飞行改进粒子群算法优化的广义回归神经网络(LPSO-GRNN)的轴重式动态汽车衡螺栓松紧状态预测模型,并结合振动信号特征提取,将该模型应用于汽车衡螺栓松紧状态的预测。首先,研究并提取了螺栓不同松紧状态下输出振动信号的波形指标、峰值指标、脉冲指标、峭度指标等信号特征,并将其作为模型的共同输入特征向量;然后,采用莱维飞行提高了粒子群优化算法的寻优能力,通过产生随机步长,提高了算法的全局寻优能力,避免算法陷入局部最优值;利用改进的算法对广义回归神经网络(GRNN)的光滑因子进行了优化,得到了全局最优的光滑因子;最后,通过设计实验,分别使用广义回归神经网络(GRNN)、粒子群算法优化广义回归神经网络(PSO-GRNN)和LPSO-GRNN,以此来对螺栓松紧状态进行了预测,并将预测结果与实际情况进行了对比分析。实验结果表明:基于LPSO-GRNN建立的螺栓松紧状态预测模型,其预测准确率可达到95%。研究结果表明:该螺栓松紧状态预测模型可以有效提高汽车衡螺栓松紧预测的准确率,同时有效解决粒子群算法容易陷入局部最优收敛的问题。Aiming at solving the matter that the internal bolts of the axle-load dynamic truck scale were loose and falling off due to the frequent loading and unloading cycle impact of heavy goods vehicles under the service state,a bolt tightening state prediction model based on the Lévy flight improved particle swarm algorithm optimization generalized regression neural network model(LPSO-GRNN model)combined with vibration signal feature extraction was proposed.And combining with the feature extraction of vibration signal,the model was applied to the state prediction of truck scale bolts.Firstly,the waveform index,peak index,pulse index,steepness index and other signal characteristics of the output vibration signal of the bolt under different tightening states were extracted,and they were jointly used as the input feature vector of the model.Then,Lévy flight was used to improve the optimization ability of the particle swarm optimization algorithm,and the global optimization ability of the algorithm was improved by generating random step sizes to avoid falling into jumping out of the local optimal value,and the smooth factor of the generalized regression neural network was optimized by the improved algorithm to obtain the global optimal smooth factor.Finally,by designing experiments,GRNN,PSO-GRNN and LPSO-GRNN were used to predict the bolt tightening state and compared it with the actual situation.The experimental comparison results show that the bolt tightening state prediction model established based on LPSO-GRNN has an accuracy of up to 95%,effectively improving the accuracy of bolt tightening prediction in weighing systems.This model addresses the problem of particle swarm optimization algorithm easily getting trapped in local optimal convergence.In conclusion,the proposed model provides an effective solution to the problem of bolt loosening and falling in dynamic axle weighing systems.

关 键 词：轴重式动态汽车衡 LPSO-GRNN预测模型 螺栓紧固 振动信号特征提取 广义回归神经网络 粒子群算法优化 莱维飞行 

分 类 号：TH715[机械工程—测试计量技术及仪器] TP183[机械工程—仪器科学与技术]