Equipment Fault Diagnosis Based on Support Vector Machine Under the Background of Artificial Intelligence

Abstract

Under modern production conditions, the scale of equipment is getting bigger and bigger, the structure is more and more complex, the function is more and more complete, and the degree of intelligence is getting higher and higher. Once the equipment system fails, it will directly affect the economic benefits, and sometimes it will produce serious social impact. Based on support vector machine technology, this paper studies equipment failure diagnosis. The basic principles of support vector machines are analyzed, the commonly used equipment failure diagnosis techniques are systematically summarized, and the equipment failure diagnosis process based on support vector machines is designed. In this paper, support vector machines are introduced into equipment failure diagnosis, using support vector machine models under different kernel functions. The computational complexity depends on the number of support vectors, avoiding the disaster of dimensionality, and having excellent generalization capabilities.