基于激光传感器采集信号的机电设备故障辨识研究  

Research on fault identification of mechanical and electrical equipment based on laser sensor signal acquisition

作　　者：张晚青 李玉根 ZHANG Wanqing;LI Yugen(Liaoning Institute of Science And Engineering,Jinzhou Liaoning 121000,China;Beijing Institute of Space Launch Technology,Beijing 100076,China)

机构地区：[1]辽宁理工学院,辽宁锦州121000 [2]北京航天发射技术研究所,北京100076

出　　处：《激光杂志》2024年第9期223-227,共5页Laser Journal

基　　金：辽宁省教育厅基本科研项目(No.JYTMS20230972)。

摘　　要：机电设备运行环境复杂,当前方法无法高精度获得机电设备故障辨识结果,而且机电设备故障辨识时间长,实时性差,为了获得更加理想的机电设备故障辨识结果,设计了基于激光传感器采集信号的机电设备故障辨识方法。首先采用激光度传感器采集机电设备工作状态信号,并对机电设备工作状态信号进行预处理,提取机电设备故障辨识的相关特征,然后将特征作为机器学习算法的输入,机电设备故障类型作为机器学习算法的输出,通过训练建立机电设备故障辨识的分类器,最后通过具体的仿真实验分析机电设备故障辨识性能。结果表明,本方法可以辨识机电设备故障,辨识精度超过95%,机电设备辨识的时间均控制在5 s以内,辨识整体效果要优于当前典型机电设备故障辨识方法。the operating environment of electromechanical equipment is complex,and current methods cannot obtain high-precision fault identification results of electromechanical equipment.In addition,the fault identification time of electromechanical equipment is long and the real-time performance is poor.In order to obtain more ideal fault identification results of electromechanical equipment,a laser sensor based signal acquisition method for electromechanical equipment fault identification is designed.Firstly,a laser degree sensor is used to collect the working status signals of electromechanical equipment,and the working status signals of electromechanical equipment are preprocessed to extract relevant features for fault identification.Then,the features are used as inputs to the machine learning algorithm,and the types of electromechanical equipment faults are used as outputs.A classifier for electromechanical equipment fault identification is established through training.Finally,the performance of electromechanical equipment fault identification is analyzed through specific simulation experiments.The results show that the method proposed in this paper can identify faults in electromechanical equipment with an accuracy of over 95%.The identification time of electromechanical equipment is controlled within 5 seconds,and the overall identification effect is better than the current typical electromechanical equipment fault identification method.

关 键 词：激光传感器 机电设备状态信号 故障辨识分类器 机器学习算法 提取特征 

分 类 号：TN209[电子电信—物理电子学]