Abstract In recent years, mechanical fault diagnosis technology at home and abroad has developed rapidly, and its application has spread to various industrial fields. Due to the complex structure of rotating machinery, the ambiguity and complexity of fault characteristics and causes are common, and it is difficult to carry out fault diagnosis. Although many researches have been carried out and some research results have been obtained, the overall diagnostic level is not very high. High, which is extremely inconsistent with the status quo that is widely used in production. Therefore, it is of great significance to carry out fault diagnosis research on rotating machinery. In this paper, this paper briefly introduces the research and application of intelligent technology in equipment fault diagnosis, and gives the superiority of fuzzy neural network technology application in equipment fault diagnosis, and expounds the basics of fuzzy theory and neural network technology. Based on the principle, the advantages and disadvantages of the two in fault diagnosis are analyzed, and the necessity of combining the two is explained. Based on the previous research on the combination of fuzzy theory and neural network, a new combination method is proposed, and a fuzzy neural network model suitable for fault diagnosis is established. A fuzzy inference method based on fuzzy network is constructed, which realizes the knowledge of information through the extraction, optimization and screening of fuzzy rules. At the same time, the fuzzy neural network learning weights are transformed into case-based reasoning-based diagnostic guidance operators, which play an important role in the rapid extraction of knowledge and improve the diagnostic accuracy. The experimental results show that compared with the commonly used neural network and fuzzy theory fault diagnosis methods, this method can make up for the shortcomings of fuzzy theory and neural network alone. It has higher diagnostic accuracy and has a good application prospect in the field of rotating machinery fault diagnosis.

中文翻译：

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神经网络算法在机械智能故障诊断中的应用

摘要 近年来，国内外机械故障诊断技术发展迅速，其应用已遍及各个工业领域。由于旋转机械结构复杂，故障特征和原因的模糊性和复杂性普遍存在，难以进行故障诊断。虽然进行了很多研究并取得了一些研究成果，但总体诊断水平不是很高。高，极不符合生产中广泛使用的现状。因此，开展旋转机械故障诊断研究具有重要意义。本文简要介绍了智能技术在设备故障诊断中的研究与应用，并给出了模糊神经网络技术在设备故障诊断中应用的优越性，阐述了模糊理论和神经网络技术的基础知识。在此原理的基础上，分析了两者在故障诊断中的优缺点，说明了两者结合的必要性。在以往模糊理论与神经网络结合研究的基础上，提出了一种新的结合方法，建立了适合故障诊断的模糊神经网络模型。构建了一种基于模糊网络的模糊推理方法，通过对模糊规则的提取、优化和筛选来实现信息知识。同时，将模糊神经网络学习权重转化为基于案例推理的诊断指导算子，对快速提取知识、提高诊断准确率起到重要作用。实验结果表明，与常用的神经网络和模糊理论故障诊断方法相比，该方法可以弥补单独使用模糊理论和神经网络的不足。具有较高的诊断精度，在旋转机械故障诊断领域具有良好的应用前景。