基于深度置信网络的液压泵内泄漏状态的诊断

Research on Diagnosing Method of Internal Leakage State of Hydraulic Pump Based on Deep Belief Network

针对液压信号的高度复杂性以及难以识别的特点,提出一种基于深度置信网络的方法用于液压泵内泄漏状态的诊断。首先利用小波变换和HHT对压力信号和流量信号进行提取特征,然后利用堆叠RBM网络对原始特征集进行优化,并提取高级的融合特征,最后使用BP进行预测。实验结果表明:DBN能够有效地提取原始特征集的内在特征,使液压信号得到了更好的表达;DBN对液压泵内泄漏状态识别精度达到了98.77%;相比于SSAE和H-ELM分类器,DBN对液压泵内泄漏状态有更好的辨识能力和稳定性。

In view of the high complexity and difficulty in identification of hydraulic signals,a method based on deep belief network was proposed for the diagnosis of leakage state in hydraulic pump.The wavelet transform and HHT were used to extract features form the pressure signals and flow signals.The stacked RBM network was used to optimize the raw feature set,and the advanced fusion features were extracted.Finally,BP was used for prediction.The experimental results show that DBN can be used to effectively extract the intrinsic characteristics of the original feature set,so that the hydraulic signals is better expressed;the DBN’s identification accuracy reaches 98.77%;compared with SSAE and H-ELM classifiers,DBN has better identification ability and stability for leakage state in hydraulic pump.