﻿ 基于小波神经网络的船舶电气故障诊断
 舰船科学技术  2023, Vol. 45 Issue (20): 172-175    DOI: 10.3404/j.issn.1672-7649.2023.20.032 PDF

Ship electrical fault diagnosis based on wavelet neural network
ZHU Zhe-hua
China Classification Society Guangzhou Branch, Guangzhou 510235, China
Abstract: In order to ensure the safe navigation of ships, it is necessary to grasp the operating status of the electrical system in real time, in order to study the ship electrical fault diagnosis model based on wavelet neural network. This model introduces wavelet analysis method into the neural network model, replaces the sigmoid function of the hidden layer of the network model with wavelet function, and designs a wavelet neural network model; This model uses wavelet adaptive soft threshold denoising to process the noise in the signal and obtain the denoised signal components containing the operational characteristics of the ship electrical system; Improve the BP neural network to achieve classification and diagnosis of ship electrical faults based on this component. The test results show that the noise reduction effect of this method is good, with an energy ratio below 0.15. The standard deviation result is above 0.922. Capable of accurately diagnosing three types of faults: tripping and jamming of the operating mechanism, overheating of the circuit, and dampness of the insulation.
Key words: wavelet neural network     ship electrical     fault diagnosis     wavelet function     noise treatment
0 引　言

1 船舶电气故障诊断分类 1.1 故障诊断分类器结构

 图 1 小波神经网络模型结构 Fig. 1 Structure of wavelet neural network model

 $\psi \left( x \right) = \cos \left( {1.75x} \right){e^{\frac{{ - {x^2}}}{2}}} 。$ (1)

 $\eta \left( u \right) = \frac{1}{{1 + {e^{ - u}}}} 。$ (2)

 $Y_l=\lambda\left[\sum\limits_{l=1}^Nw_{jl}\psi_{a,b}\left(\sum\limits_{l=1}^Nw_{ij}\boldsymbol{X}_i\right)\right]。$ (3)

1.2 基于小波阈值的信号降噪

 ${\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\gamma } _{j,k}} = \left\{ \begin{gathered} {{\rm{sgn}}} \left( {{\gamma _{j,k}}\left( {\left| {{\gamma _{j,k}}} \right| - \xi } \right)} \right),\left| {{\gamma _{j,k}}} \right| \geqslant \xi，\\ 0,\left| {{\gamma _{j,k}}} \right| < \xi。\\ \end{gathered} \right.$ (4)
 $\xi = \mu \sqrt {2{lnK} } 。$ (5)

1.3 电气故障分类诊断

 $G = \frac{r}{{m + p}}。$ (6)

 $H = \frac{1}{2}\sum\limits_{i = 1}^3 {\sum\limits_{j = 1,j \ne i}^3 {\left( {{K_i} \times {M_i} \times {N_i}} \right)} } 。$ (7)

 $\tilde f = \frac{1}{{\chi \left( {T - {y_2}} \right)}}。$ (8)

2 实验结果分析

 $\varphi = \frac{{\sqrt {\displaystyle\sum\limits_i {x_i^2} } }}{{\sqrt {\displaystyle\sum\limits_i {s_i^2} } }} ，$ (9)
 $\sigma = \sqrt {\sum\limits_i {{{\left( {{s_i} - {x_i}} \right)}^2}} }。$ (10)

 图 2 操作机构脱扣卡滞故障诊断结果 Fig. 2 Diagnosis results of operating mechanism tripping and jamming fault

 图 3 电路过热故障诊断结果 Fig. 3 Diagnosis results of circuit overheating fault

 图 4 绝缘体受潮故障诊断 Fig. 4 Diagnosis of insulator moisture fault
3 结　语

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