A Comparative Study on Algorithm of Rolling Bearing Speed-Vibration Deep Learning Model
Bearing health condition directly affects the stability and safety of machinery,fault diagnos is of bearing running state is particularly important.Based on this,Levenberg-Marquardt(LM)algorithm,Bayesian Regularization(BR)algorithm and Quantum Conjugate Gradient(QCG)algorithm are selected to train and test the vibration data of rolling bearings under the condition of time varying acceleration under three different conditions:health,inner ring fault and outer ring fault.Different types of deep learning models are constructed in the MATLAB R2023b software,and the mean square error value,regression R value,training time and training rounds of the deep learning model are compared and analyzed.After analysis,it is concluded that the Bayesian regularization algorithm should be selected to train the deep learning network model when the precision and accuracy,memory resources and time are sufficient.
rolling bearingspeed-vibrationdeep learning modelLevenberg-Marquardt(LM)algorithmBayesian Regularization(BR)algorithmQuantum Conjugate Gradient(QCG)algorithm
NETL
NSTL
万方数据
维普
