Application of SSA-SqueezeNet in Bearing Damage Degree Identification
Due to the non-linear and non-stationary characteristics of bearing vibration signals under variable load conditions,the complex features of bearing damage degree are difficult to extract.A rolling bearing damage degree identification method based on sparrow search algorithm(SSA)combined with lightweight convolutional neural network(SSA-SqueezeNet)was proposed.Firstly,the fault diagnosis method used SSA to adaptively adjust the hyperparameters in the SqueezeNet network so that the network structure reduced fluctuations while improving accuracy.Secondly,the rolling bearing one-dimensional vibration signal was input to the optimized network after continuous wavelet transform for training.Finally,the proposed method was validated by using Case Western Reserve University rolling bearing experimental data and XJTU-SY rolling bearing accelerated life test data.Compared with other diagnosis methods,the results show that the optimized network model can accurately identify the damage degree and life state of rolling bearings,and has strong cross-load adaptive capability and generalization performance.
rolling bearingfault diagnosishyperparameter optimizationsparrow search algorithmSqueezeNet
NETL
NSTL
万方数据
