提出了一种新型分类器用于转子系统故障诊断，包括基于独立分量分析的特征提取和基于多层感知器的模式分类。通过ICA的使用，特征向量可以从不同运行方式下（旋转时的速度和/或负载）采集到的多通道振动测量信号中完整的提取出来。因此，一个强大的对运行环境变化不敏感的并联MLP分类器就建立了。实验结果表明嵌入在振动观测数据中的固有的故障特征能够被有效的捕获并且不同的故障形式（例如不平衡、基座的紧或松等）能够被正确的分类，两者都意味着提出的ICA-MLP分类器在转子系统故障诊断中有很大的潜力。“,”A novel classifier is proposed for fault diagnosis of rotor system, with independent component analysis (ICA) based feature extraction and multi-layer perceptron (MLP) based pattern classification. By the use of ICA, feature vectors are integratedly extracted from multi-channel vibration measurements collected under different operating patterns (in term of rotating speed and/or load). Thus, a robust multi-MLP classifier insensitive to the change of operation conditions Is constructed. Experimental results indicate invariable fault features embedded in vibration observations can be effectively captured and different fault patterns (for example imbalance, impact and loose foundation) can be correctly classified, both of which imply great potential of the proposed ICA-MLP classifier in fault diagnosis of rotor system.