In response to the complexities of fault signal transmission path，instability and difficulties in extracting fault feature for aircraft engine main bearing，a fault recognition method was proposed based on the fusion of time-domain feature parameters，frequency-domain feature parameters and intrinsic mode function （IMF） energy moment feature parameters for dimensionality reduction.Firstly，60 groups of bearing rolling element fault，inner ring fault，outer ring fault and bearing without fault data were selected respectively then time-domain，frequency-domain and energy moment features were extracted from these instances.Addressing the issue of high dimensionality，extensive data and redundant information of the fusion vector composed of three parameters，principal component analysis （PCA） was employed to reduce the dimensionality of these data and effective principal components were extracted based on cumulative contribution rates of principal components.Finally，the dimensionality reduction feature vectors were input into the support vector machine （SVM） for pattern recognition to diagnose the types of bearing faults.The results demonstrate that compared to models employing single feature parameters，this method effectively extracts fault feature vectors from complex signals.Subsequently，it accurately identifies and classifies fault types using these feature vectors，achieving a fault recognition rate of 98.75%.