基于激光干涉技术的设备状态监测与故障快速诊断研究

Research on equipment condition monitoring and fast faultdiagnosis based on laser interference technology

研究基于激光干涉技术的设备状态监测与故障快速诊断,提升设备故障诊断精度以及实时性。采用激光自混合干涉技术采集设备状态振动信号,利用卡尔曼滤波方法对所采集设备状态振动信号实施滤波处理,选取关联规则挖掘方法Apriori算法利用已滤波的设备状态信号实现设备状态监测与故障快速诊断,Apriori算法通过迭代扫描事务数据库挖掘符合支持度条件的频繁集,利用所获取频繁集建立关联规则,通过更新规则库内规则可信度以及故障类型数量更新设备故障诊断专家库,实现设备状态监测与故障快速诊断。实验结果表明,采用该方法诊断设备故障精度高于99.5%,诊断设备故障的时间开销低于200 ms,可实现设备状态有效监测。

Research on equipment condition monitoring and rapid fault diagnosis based on laser interference technology to improve the accuracy and real-time performance of equipment fault diagnosis.Laser self-mixing interference technology is used to collect equipment state vibration signals,Kalman filter method is used to filter the collected equipment state vibration signals,and association rule mining method Apriori algorithm is selected to realize equipment state monitoring and rapid fault diagnosis using filtered equipment state signals,Apriori algorithm mines frequent sets that meet the support conditions by iteratively scanning the transaction database,the obtained frequent sets is used to establish association rules,and the equipment fault diagnosis expert database is updated by updating the credibility of the rules in the rule library and the number of fault types to realize equipment status monitoring Quick diagnosis with faults.Experimental results show that the accuracy of diagnosing equipment faults using this method is higher than 99.5%,and the time cost of diagnosing equipment faults is less than 200ms,which can realize effective monitoring of equipment status.