Fault Diagnosis of the Planetary Gearbox Based on ssDAG-SVM

Abstract Planetary gearbox is of great significance for many practical cases, and many data-driven approaches have been employed to solve the fault diagnosis problem for the system. Among these methods, Directed Acyclic Graph Support Vector Machines (DAG-SVM) has been widely adopted due to its ability to handle the multi-class problem. Different from traditional DAG-SVM, a structure-selected DAG-SVM (ssDAG-SVM) is proposed such that the diagnosis performance will not degrade because of inappropriate node structure. By introducing the concept of class separability, the principle of evaluating the degree of class separability is integrated into the process of constructing the DAG-SVM structure. Subsequently, a proper structure can be selected to realize the planetary gearbox fault diagnosis with high accuracy. Finally, the effectiveness of the method is illustrated by some practical experiments.