Large Rotating Machinery Fault Diagnosis and Knowledge Rules Acquiring Based on Improved RIPPER
The data of fault monitoring for large rotating machine are large and noisy, there are some relationships between properties and property values, which are coincide with the rules of data mining technology. The data mining technology was studied in order to obtain the laws and classify the faults. The improved RIPPER (Repeated Incremental Pruning to Produce Error Reduction ) data mining rule learning algorithm was studied for large rotating machine, the rules set files were obtained by analyzing the fault samples and updated in time. The extracted knowledge rules could also be used as the real time diagnosis of common faults.
fault monitoring large rotating machine data mining improved RIPPER real time diagnosis)
Sun Jianghong Xu Xiaoli
School of ElectroMechanical Engineering Beijing Information Science & Technology niversity Beijing, School of ElectroMechanical Engineering Beijing Information cience & Technology University Beijing,
国际会议
长沙
英文
1501-1504
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）