基于深度信念网络的滚动轴承特征迁移诊断

Feature Transferring Diagnosis of Rolling Bearing Based on Deep Belief Network

滚动轴承的故障智能诊断研究多是针对同源数据进行,而不同型号、不同工况下的滚动轴承,由于时、频特征差异,加之背景噪声的影响,导致识别准确率偏低。为了解决这一问题,笔者以6307和6205两类深沟球轴承为研究对象,建立了以深度信念网络(deep belief network,简称DBN)为核心的迁移诊断模型,构造了以波形指标、峭度指标、近似熵及分散熵为代表的特征识别参数。为了抑制信号传递路径(共振频带差异)和背景噪声的影响,引入最大相关峭度反卷积(maximum correlated kurtosis deconvolution,简称MCKD)方法,并对其关键参数实施了自适应选取。结果表明,由MCKD与DBN联合组成的迁移诊断模型,在3类不同数据源之间的诊断准确率均超过了95%,为滚动轴承的迁移特征诊断提供了一条可行的途径。

Fault intelligent diagnosis of rolling bearing is mostly based on the same data source.Due to the difference of time and frequency characteristics and the influence of background noise,the accuracy of identifying different models and different working conditions of rolling bearing is low.In order to solve this problem,two kinds of deep groove ball bearings,6307 and 6205,are taken as the research objects in this paper.A transfer diagnosis model with deep belief network(DBN)as the core is established,and the characteristic identification parameters represented by waveform index,kurtosis index,approximate entropy and dispersion entropy are constructed.The maximum correlated kurtosis deconvolution(MCKD)method is introduced to suppress the influence of signal transmission path(resonance band difference)and background noise.Results show that the accuracy of the transfer diagnosis model,which is composed of MCKD and DBN,is more than 95%among the three different kinds of data,providing a feasible way for the transfer diagnosis of rolling bearing.