To effectively reduce the dimension of rolling bearing fault features and improve the ac-curacy of diagnosis,the PCA and SVM were applied in the fusion of bearing fault features,and the cor-responding decision-making process was presented.By using the fault feature extraction algorithm and eigenvector constructing methods which were proposed based on wavelet packet decomposition,the bearing vibration signals in different states were decomposed to get the 8-dimensional feature sets which could be used to characterize the running conditions of the bearing.The cumulative contribution rate of 95% principal components were extracted by using PCA method and were input into SVM clas-sifier for identification.Results show that the fault feature dimensions of rolling bearing can be re-duced from 8-dimensions to 5-dimensions,which can still characterize the bearing status effectively, and the computational complexity can be reduced.The fault diagnosis accuracy is higher than 97%,and the diagnosis time is short relatively.The identification accuracy of four bearing status from high to low in turn is normal,outer ring peel,roller peel and inner ring peel.It can ensure the safe operation of the equipment and provide theoretical basis for fast fault diagnosis.%为有效降低滚动轴承故障特征的维数并提高诊断准确率，将主成分分析(PCA)和支持向量机(SVM)方法应用到轴承故障特征的融合分析中，给出了相应的决策流程。应用基于小波包分解的特征提取算法及特征向量的构造方法对不同状态下的振动信号进行分解，得到用于表征轴承运行状态的8维特征集合；应用 PCA 提取累积贡献率达到95%的特征主成分并输入 SVM 分类器中进行识别。结果表明，将滚动轴承故障特征从8维降低到5维，仍可有效表征轴承的状态，但大大降低了计算的复杂性；故障诊断的准确率达到97%以上，诊断时间也相对较短；4种轴承状态识别的准确率从高到低依次为正常、外圈剥落、滚动体剥落和内圈剥落，可为确保设备安全运行和快速故障诊断提供理论依据。
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