关键词: 旋转机械, 故障诊断, 知识获取, 规则挖掘, 重复增量修剪减少误差方法（RIPPER）

Abstract: Data mining technology could extract the un-known natural rules from the amount,incomplete,noise,fuzzy and random practical information.The data mining technology is studied in order to obtain the fault diagnosis knowledge of rotating machinery effectively.A new fault diagnosis model is proposed based on data mining.The RIPPER（Repeated Incremental Pruning to Produce Error Reduction） data mining rule learning algorithms are studied by comparison from all aspects such as prediction accuracy,learning efficiency and robustness.Through collected the fault information and created the fault samples,the rules set files are obtained by using the RIPPER rule-learning algorithm and the intelligent diagnosis knowledge rules are updating in time.Finally the fault type is determined.The extracted knowledge rules can also be used as the real time diagnosis.It is used for vibration signal data mining in engineering field concerned with the frequency domain successfully.

Key words: rotating machinery, fault diagnosis, knowledge acquisition, rule mining, RIPPER