基于人工鱼群算法的船舶电子设备故障智能诊断方法

Intelligent fault diagnosis method of ship electronic equipment based on artificial fish swarm algorithm

为实现电子设备的高效维护,确保船舶安全航行,设计了基于人工鱼群算法的船舶电子设备故障智能诊断方法。采用离散小波变换法分解电子设备运行信号样本,通过计算不同尺度下的小波能量值完成船舶电子设备故障特征参数的提取,将其作为基于RBF神经网络的故障诊断模型的输入,利用人工鱼群算法对故障诊断模型的权值、阈值参数作优化处理,最终输出不同类型故障发生概率,实现电子设备故障诊断。实验结果表明,正常以及不同故障状态下,电子设备运行信号的时域波形存在很大差异,研究方法可实现故障特征参数的提取,并完成故障类型的识别,30次迭代后MSE指标即可降至最低,仅为10^(-4)。

Research on the intelligent diagnosis method for ship electronic equipment faults using artificial fish swarm algorithm,to achieve efficient maintenance of electronic equipment and ensure safe navigation of ships.The discrete wavelet transform method is used to decompose the operating signal samples of electronic equipment.After extracting the fault feature parameters of ship electronic equipment by calculating the wavelet energy values at different scales,it is used as input for the fault diagnosis model based on RBF neural network.The artificial fish swarm algorithm is used to optimize the weight and threshold parameters of the fault diagnosis model,and ultimately output the probability of different types of faults to achieve electronic equipment fault diagnosis.The experimental results show that there are significant differences in the time-domain waveforms of electronic equipment operating signals under normal and different fault states.The research method can extract fault feature parameters and complete the identification of fault types.After 30 iterations,the MSE index can be reduced to the lowest,only 10^(−4).