Transfer Learning-Based Fault Diagnosis Method for Marine Turbochargers

To address the issues of the high cost of marine turbocharger fault simulation testing and the difficulties in obtaining fault sample data, a multi-body dynamics model of a marine turbocharger was developed. The simulation approach was used to acquire the turbocharger vibration signals. The result shows that the amplitude of the 1x vibration signal power spectrum drops as the bearing surface roughness increases. However, the amplitude of the 2x and 9x vibration signal power spectra increases as the roughness increases. The TrAdaBoost transfer learning method is used to develop a marine turbocharger diagnosis model. The validation results of 2040 simulated fault samples reveal that when the desired sample number is 20, the diagnostic model has an accuracy of 87%. When the desired number of samples is 40, the diagnostic model's accuracy is 96%. The diagnosis model may perform diagnosis information transfer between the actual turbocharger and the simulation model.