Electromechanical Coupling Fault Diagnosis Model of Marine Electric Thruster Based on TCNN
Motor and gearbox are essential functional components of marine electric thruster,and their faults seriously affect the safety of the thruster and even the whole ship.Due to the influence of electromechanical coupling,when simultaneous failures occur in both the motor and gearbox,the signal-to-noise ratio of the fault signal is low,and the fault characteristics are crossed.In order to diagnose the coupling fault between the two,a Two-Branch Convolutional Neural Network(TCNN)model of One-Dimensional Convolutional Neural Network(1DCNN)and Two-Dimensional Dilated Convolutional Neural Network(2D-DCNN)is proposed,which deeply extracts the global and detailed features of current data through convolution kernels of different scales.In terms of data preprocessing,the two-dimensional grayscale image construction method is improved to enhance the continuity of signal time series,and the expansion factor is introduced into the 2D-DCNN channel to extract the global information in the signal without increasing the amount of calculation.The exponential decay is used to ensure that the model can stably approximate the optimal solution in the iterative cycle.The experimental results show that the TCNN model has better diagnostic performance than other models,and its diagnostic accuracy can reach 99.8％.At the same time,the diagnostic accuracy rate of the model is not less than 98.5％in different working environments,which has good adaptability and robustness.The research results provide new ideas and methods for solving the problem of electromechanical coupling fault diagnosis of marine electric thrusters.
marine electric thrusterfault diagnosistwo-branch convolutional neural networkmotorgearboxelectromechanical coupling fault
万方数据
维普
