Application of neural network algorithm in fault diagnosis of mechanical intelligence

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.