Machinery Fault Diagnosis Technology Based on Artificial Intelligence Technology

Abstract

My country’s industrial field has developed rapidly since modern times, and the technology has become more and more mature. Industrial technology is gradually developing in the direction of automation, large-scale and systematization. In the context of the rapid development of industrial technology, some multifunctional mechanical facilities have been invented continuously, which has brought great convenience to industrial production. However, with the development of science and technology, mechanical facilities have more and more functions, and the components of mechanical equipment are becoming more and more complicated. Mechanical failures have become inevitable, so the safety of mechanical equipment it has become a key issue that needs to be solved at the moment, and people are putting more and more energy in this area. In order to ensure the normal progress of industrial production, it is necessary to carry out inspection and maintenance in daily operation to ensure the normal operation of machinery. At present, the most traditional mechanical fault diagnosis method in our country is to collect the vibration signal generated by the operation of the mechanical equipment, and conduct a comprehensive analysis of the signal data collected during the detection process for fault diagnosis. However, the current social development speed is also changing with each passing day, and the mechanical equipment is becoming more and more complex, which has led to the gradual increase in the difficulty of data collection and storage. It is difficult to obtain accurate fault signals through traditional fault diagnosis methods to accurately carry out massive fault data. Analysis and processing. Based on this background, deep learning artificial intelligence algorithms are used to carry out deeper essential characteristics of massive fault data. Data mining is carried out on the basis of raw data information. Compared with traditional methods, this method can be more accurately described. The fault data allows us to accurately judge mechanical faults. In this paper, based on the industrial development background of artificial intelligence technology and mechanical big data, the fault detection problem of my country’s current mechanical equipment is studied, and the appropriate algorithm is selected to apply to the detection of mechanical equipment to realize the optimization of fault diagnosis. The experimental results show that the combination of artificial intelligence technology and traditional mechanical fault diagnosis technology can greatly improve the diagnostic efficiency of mechanical equipment, which has a driving force that cannot be ignored for my country’s industrial development.