基于IHHO-BP神经网络的模拟电路故障诊断

Fault diagnosis of analog circuit based on IHHO-BP neural network

针对模拟电路故障类型多、故障状态不稳定以及故障数据冗余,使得模拟电路故障诊断困难的问题,提出利用改进哈里斯鹰算法(improved Harris Hawks optimization, IHHO)优化反向传播(back propagation, BP)神经网络,实现模拟电路故障特征选择与诊断。首先,将非线性自适应因子、柯西变异和随机差分扰动引入哈里斯鹰算法,实现收敛速度和精度的提升;其次,采用IHHO对模拟电路的单一故障和组合故障仿真数据进行特征选择,完成数据预处理;最后,采用IHHO-BP算法,对预处理后的故障数据进行训练和测试,实现模拟电路故障诊断。诊断结果表明,所提方法的诊断精度相较于其他算法提升了5.5%。

The improved Harris Hawks optimization algorithm(IHHO)is proposed to solve the problem that analog circuit fault diagnosis is difficult due to multiple fault types,unstable fault states and redundant fault data.IHHO optimized back propagation(BP)neural network to realize fault feature selection and diagnosis of analog circuits.Firstly,the nonlinear adaptive factor,Cauchy variation and stochastic difference perturbation are introduced into the Harris Hawks optimization algorithm to improve the convergence speed and accuracy.Secondly,IHHO is used to select the characteristics of the single fault and the combined fault simulation data of the analog circuit to complete the data preprocessing.Finally,IHHO-BP algorithm is used to train and test the preprocessed fault data to realize the fault diagnosis of analog circuits.The diagnostic results show that the proposed method improves the diagnostic accuracy by 5.5%compared with other algorithms.