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中文摘要：
多故障作为标准单故障的组合，很多文献对多故障的诊断都提出行之有效的解决策略，但却忽视单故障模式之间的相互关系，而影响到多故障诊断效率，尤其对于故障繁多的复杂产品．为了克服该缺点，引入KFCM—F算法和核化聚类有效性指标K坛，提出两阶段聚类框架，数据仿真试验证明该框架能有效发现单故障之间的潜在关系，从而达到压缩故障模式以期提高诊断效率的目的．
英文摘要：
Many literatures proposed effective solutions to multi-faults diagnosis as the combination of standard single faults. However, ignoring interrelation among single faults is to impair its efficiency, especially for complex products. To overcome these disadvantages, two-stage clustering frame was proposed by using KFCM-F algorithm and kernel-based cluster validity index KVK. The simulation results validate the effectiveness of this frame to find latent interrelation among single faults and reduce the number of fault pattern for improving diagnostic efficiency.
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