基于SOM神经网络的摩擦状态识别研究

Research on Friction State Recognition Based on SOM Neural Network

为提取摩擦振动的特征和实现摩擦副摩擦状态的识别,在往复摩擦磨损试验机进行摩擦副混合摩擦和干摩擦状态的摩擦磨损试验。应用谱减法对试验采集的摩擦振动信号进行降噪,计算降噪后的摩擦振动15个特征参数。应用自组织映射(Self-organizing map, SOM)神经网络对摩擦副不同摩擦状态的摩擦振动特征参数进行分析,得到摩擦振动的SOM神经网络神经元分类。研究结果表明,谱减法能消除摩擦磨损试验机的背景噪声,SOM神经网络算法能够有效分析摩擦振动信号的特征,实现摩擦副摩擦状态的识别。

In order to extract the characteristics of frictional vibrations and realize the recognition of friction states for friction pair,the friction and wear tests of mixed friction and dry friction states of the friction pair were carried out using a reciprocating friction and wear testing machine.Frictional vibration signals collected during the tests were denoised by applying the spectral subtraction,and 15 characteristic parameters of the friction vibration after noise reduction were calculated.The neuron classification of the self-organizing map(SOM)neural network was obtained by analyzing the characteristic parameters of the frictional vibration of friction pair in different friction states with SOM neural networks.The results demonstrate that the spectral subtraction method can eliminate the background noises of the friction and wear testing machine,and the SOM neural network algorithm can effectively analyze the features of the frictional vibration signals and realize the recognition of the friction state of the friction pair.