Inter-turn short-circuit diagnosis of wound-field doubly salient machine using multi-signal fusion and GA-XGBoost

The wound-field doubly salient machine (WFDSM) is a generating system core assembly. Its condition monitoring and early fault diagnosis are key to improving system reliability. This study proposes a fault diagnosis method based on multi-signal mixed domain fusion at the feature level and genetic algorithm improved XGBoost (GA-XGBoost). First, low-pass noise reduction, singular value decomposition noise reduction, and other signal pre-processing are applied to the current and vibration signals of early inter-turn short-circuit faults. Second, the time domain, frequency domain, and entropy features of the current signal, along with the time domain features of the vibration signal, are extracted, together forming a diagnostic feature set. Then, the feature set is put into the GA-XGBoost model. The results show that the proposed method of feature fusion achieves an accuracy of 99.3%. Thus, the multi-signal mixed domain fusion has stronger signal characteristic expression ability. In addition, the GA-XGBoost model achieves better generalizability and higher accuracy in the small-scale samples of WFDSM faults. The experimental results demonstrate that this method can effectively diagnose various conditions and also has strong anti-interference capability under extreme conditions.