Comparison Study on Parametric Fault Diagnosis Using BPNN, SVM and SDAE for DC-DC Converters in Aircraft

Effective fault diagnosis for mission-critical and safety-critical systems, such as aircraft electric power system, has been an essential and mandatory technique to reduce failure rate and prevent unscheduled shutdown. This paper aims to compare the performance of three efficient fault classifiers, BPNN, SVM and SDAE, in parametric fault diagnosis for the boost DC-DC converter in aircraft. The training set and test set are collected based on the fitting of NASA datasets of electrolytic capacitors/MOSFET and a boost DC-DC converter simulation system. Effective fault features are extracted from four node signals using time-domain and statistical analysis. Seven kinds of faults of electrolytic capacitor and power MOSFET were studied. The simulation results show that SVM and SDAE have a higher classification accuracy for parametric faults, such as the component degradation of electrolytic capacitor and power MOSFET, but BPNN has fast diagnosis, more suitable for cases with small data volume.