针对船舶模拟电路元件复杂交互，故障信号在大量的正常信号中难以凸显，故障特征提取识别难度较大的问题，提出基于RBF神经网络的船舶模拟电路故障诊断方法。由基于小波包的船舶模拟电路故障特征提取方法，以小波分解重构的方式，捕捉电路频带能量变化特征；使用基于状态转移算法优化RBF神经网络的故障诊断模型，由状态转移算法优化RBF神经网络参数，构建用于诊断电路故障的RBF神经网络模型后，学习所提取故障特征与类型之间关系，诊断新输入的船舶模拟电路输出信号故障类型。实验测试结果显示，此方法在有效捕捉船舶模拟电路故障频带能量变化特征后，对多种船舶模拟电路故障的诊断结果均未出现明显错误。
A fault diagnosis method for ship analog circuits based on RBF neural network is proposed to address the complex interaction of components in ship analog circuits, the difficulty of highlighting fault signals in a large number of normal signals, and the difficulty of extracting and identifying fault features. The fault feature extraction method for ship simulation circuits based on wavelet packets captures the energy variation characteristics of the circuit frequency band through wavelet decomposition and reconstruction; Using state transition algorithm to optimize the fault diagnosis model of RBF neural network, optimizing the parameters of RBF neural network, constructing an RBF neural network model for diagnosing circuit faults, learning the relationship between extracted fault features and types, and diagnosing the new input ship analog circuit output signal fault type. The experimental test results show that after effectively capturing the energy changes in the frequency band of ship analog circuit faults, this method has not shown significant errors in the diagnosis of various ship analog circuit faults.
2024,46(10): 182-185 收稿日期：2023-09-27
DOI：10.3404/j.issn.1672-7649.2024.10.033
分类号：TN710
基金项目：2021年辽宁省教育厅基本科研项目（青年项目）（LJKQZ2021124）
作者简介：霍艳飞(1972-),女,硕士,高级实验师,研究方向为电力控制技术
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