Graph-based semi-supervised random forest for rotating machinery gearbox fault diagnosis

Random forest (RF) is an effective method for diagnosing faults of rotating machinery. However, the diagnosis accuracy enhancement under insufficient labeled samples is still one of the main challenges. Motivated by this problem, an improved RF algorithm based on graph-based semi-supervised learning (GSSL) and decision tree is proposed in this paper to improve the classification accuracy in the absence of labeled samples. The unlabeled samples are annotated by the GSSL and verified by the decision tree. The trained improved RF model is applied to the fault diagnosis for the rotating machinery gearbox. The effectiveness of the proposed algorithm is verified via hardware experiments using a wind turbine drivetrain diagnostics simulator (WTDDS). The results show that the proposed algorithm achieves better accuracy of classification than conventional methods in gearbox fault diagnosis. This study leads to further progress in the improvement of machine learning methods with insufficient and unlabeled samples.

Automated summary

An improved random forest (RF) algorithm based on graph-based semi-supervised learning (GSSL) and decision tree is proposed in this paper to enhance classification accuracy under insufficient labeled samples. The effectiveness of the algorithm is verified through hardware experiments, showing better accuracy than conventional methods in gearbox fault diagnosis, leading to further progress in machine learning with insufficient and unlabeled samples.