DT Algorithm in Mechanical Equipment Fault Diagnosis System

With the rapid development of China's economy, all kinds of machinery and equipment in the industrial field are developing in the direction of high concentration and refinement. The precise cooperation between a variety of mechanical equipment makes the entire mechanical system run safely and smoothly. Therefore, the importance of the safe operation of each equipment is self-evident. The purpose of this paper is to study the application of decision tree algorithm(DTA) in machinery equipment fault diagnosis(FD) system. The analysis principle and construction process of the DTA are introduced. On this basis, the optimization of the DTA model is proposed. Tested on the Weka machine learning platform, compared with the traditional ID3 decision tree (DT) construction algorithm, the DT structure constructed by the algorithm in this paper is simple, which improves the generalization ability of the DT, and also has a certain ability to suppress noise. When β = 0.58, the classification accuracy of the algorithm in this paper is above 90%. Using the improved DTA proposed in this paper, a set of mechanical equipment FD system is constructed, and the historical data of the motor is analyzed by the DTA.