Research on gearbox fault detection model based on ridge regression and decision tree

Abstract

Gearbox is widely used in mechanical equipment and plays an important role in mechanical transmission. Therefore, it is necessary to detect and diagnose the fault of the gearbox in time. This paper needs to establish a fault detection model of the gearbox to detect whether the gearbox is in a fault state. Because the result can only be yes or no, the ridge regression model is first established. However, because the difference between the original sample data is not obvious, the accuracy of the obtained ridge regression model is low. Therefore, this study extracts the features of the data of the four parts, and defines five indicators: effective value, pulse index, skewness index, margin index and kurtosis index. The decision tree model is established with 70 % of the sample data. Firstly, the depth of the largest tree is set to 5. Secondly, the importance of the feature is determined according to the size of the Gini value, and the fault detection decision tree model is constructed. Finally, the model is tested with 30 % of the test data, and the accuracy is 91.17 %. The precision rate is 88.9 %, and the recall rate is 94.12 %. It is considered that the model is more reliable.