Department of Electrical Engineering, Jiyuan Vocational and Technical College, Jiyuan 459000, China.
Comput Intell Neurosci. 2022 Aug 21;2022:8358794. doi: 10.1155/2022/8358794. eCollection 2022.
In order to improve the accuracy of electrical equipment failure diagnosis and keep electrical equipment operating safely and efficiently, this paper proposes to design an electrical equipment failure diagnosis system based on a neural network, analyze the faults of electrical equipment and their causes, and establish knowledge base according to relevant data and expert judgment. The fault knowledge base was introduced into the neural network operation structure, and the fault diagnosis results were classified step by step through multiple subnetworks. In data preprocessing, in order to avoid the redundancy of primary fault information features, the principal component heuristic attribute reduction algorithm was used to select the fault data samples optimally. The neural network learning algorithm is used to calculate the forward direction and error rate of the initial error data, and the reliability function is used to optimize the initial weight threshold of the neural network, propagating the error backwards and high. Experimental results show that adding attribute reduction improves error classification performance, avoids the problem of local minima through neural network operation, and has fewer iteration steps, lower average error, and higher accuracy of fault diagnosis, reaching 95.6%.
为了提高电气设备故障诊断的准确性，确保电气设备安全、高效运行，本文提出了一种基于神经网络的电气设备故障诊断系统设计方案。该方案分析了电气设备的故障及其原因，并根据相关数据和专家判断建立了故障知识库。将故障知识库引入神经网络的运算结构，通过多个子网逐步对故障诊断结果进行分类。在数据预处理阶段，为了避免原始故障信息特征的冗余，使用主成分启发式属性约简算法对故障数据样本进行了优化选择。神经网络学习算法用于计算初始错误数据的正向传播和错误率，可靠性函数用于优化神经网络的初始权重阈值，反向传播误差并进行优化。实验结果表明，添加属性约简可以提高错误分类性能，通过神经网络运算避免局部最小值问题，且迭代步骤更少，平均误差更低，故障诊断的准确率更高，达到 95.6%。
