Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved LightGBM

The turnout switch machine is the critical equipment of the signal system, which has a significant influence on the efficiency and safety of train operation. However, most fault diagnosis technologies of the switch machine are difficult to distinguish samples with similar categories, which leads to the low diagnostic accuracy. Thus, a fault diagnosis method based on improved LightGBM is proposed to deal with the above problems. Time domain features and multi-scale permutation entropy are extracted to capture the weak fault. Moreover, an adaptive feature selection (AFS) method is presented to reduce redundant features. Especially an improved Focal Loss (IFL) function is established, which improves the ability to distinguish samples of similar features in a multi-classification model. The three-phase action current from the switch machine is utilized to testify to the proposed method and compare it with other methods. The experimental results show that the diagnosis accuracies of this method in the normal-reverse and reverse-normal conversion process reach 98.47 % and 96.09 %, respectively, which is well-suitable for practical application.