电力机械关键零件加工状态检测与分析  

Inspection and Analysis of Machining Status of Key Parts of Power Machinery

作　　者：康群英 刘劲涛 王琳 KANG Qun-ying;LIU Jin-tao;WANG Lin(School of Power and Energy,Shenyang Institute of Engineering,Shenyang 110136,Liaoning Province;College of Mechanical Engineering,Shenyang Institute of Engineering,Shenyang 110136,Liaoning Province)

机构地区：[1]沈阳工程学院能源与动力学院,辽宁沈阳110136 [2]沈阳工程学院机械学院,辽宁沈阳110136

出　　处：《沈阳工程学院学报（自然科学版）》2022年第3期77-85,共9页Journal of Shenyang Institute of Engineering:Natural Science

基　　金：国家自然科学基金青年基金支持(62001312)。

摘　　要：针对电力机械的关键零件在加工中会发生磨损等缺陷的问题,提出了对关键零件进行在线检测与分析。采用间接检测的方法,利用加速度传感器、INV3018CT采集仪和DASP软件对关键零件上的振动信号进行采集与分析,使用Matlab软件进行时域、频域和联合时-频分析;同时,运用小波变换功能,利用小波降噪剔除原始数据中的有害噪声,提高信噪比。然后,通过Smoothing Spline对离散试验数据进行函数拟合,找出关键零件的振动特性,提取有效特征值,并选取缺陷的特征向量,建立健康诊断模型。最后,通过Simulink仿真模块对关键零件的加工状态进行模拟,并与实际检测的振动信号进行对比,得到了一致结果,确认了该方案的可行性。Aiming at the problem of wear and tear of key parts of electric machinery in machining,the on-line detection and analysis of the key parts are proposed.Indirect detection method is adopted,acceleration sensors are installed in key parts,and INV3018CT acquisition instrument is connected.Vibration signals are collected and analyzed by DASP software,and time-domain,frequent-domain and joint time-frequency analysis are carried out by Matlab software.At the same time,the wavelet transform is used to eliminate the harmful noise in the original data and improve the signal-to-noise ratio.By means of function fitting of discrete test data by Smoothing Spline,vibration characteristics of key parts can be obviously found out,effective characteristic values can be extracted,and eigenvectors of wear and failure can be selected,so as to establish a health diagnosis model of key parts.Finally,through Simulink simulation module to simulate the processing status of key parts,and compared with the actual detected vibration signal,its verification results are consistent,further confirm the feasibility of the scheme.

关 键 词：关键零件 振动信号 小波变换 健康诊断模型 SIMULINK仿真 

分 类 号：TH113.1[机械工程—机械设计及理论]