论文部分内容阅读
针对高速自动机状态监测和故障诊断中,传感器数目难以确定,测点定位困难的问题,提出了应用粒子群优化模态置信准则的高速自动机测点优化配置方法。通过对某型高射机枪高速自动机有限元振动模态分析结果,结合试验模态测试,识别出高速自动机的振型及模态参数,并以此作为依据初选5个测点,并构建基于模态置信准则的适应度函数,通过采用该适应度函数的粒子群优化算法对这5个测点进行优化配置,解决了高速自动机机箱振动信号采集过程中测点的择优布置以及传感器定位困难及数量难以估计的问题。
Aiming at the problem that the number of sensors is hard to be determined and the measuring points are difficult to locate in the condition monitoring and fault diagnosis of high speed automatic machines, a new optimal measuring point allocation method based on the particle swarm optimization modal confidence criterion is proposed. Through the finite element vibration modal analysis results of a certain type of high-speed machine gun high-speed automatic machine, combined with the test modal test, the modal and modal parameters of the high-speed automaton are identified and used as the basis to select 5 measuring points and construct Based on the fitness function of the modal confidence criterion, the particle swarm optimization algorithm using the fitness function is used to optimize the configuration of the five measuring points, which solves the problem of the optimal placement of the measuring points and sensor location during the vibration signal acquisition process of the high-speed automata Difficult and difficult to estimate the number of issues.