An Improved Random Forest Algorithm of Fault Diagnosis for Rotating Machinery

In this paper, a semi-supervised random forest (RF) algorithm is presented for fault diagnosis of rotating machinery. Firstly, many unlabeled samples are divided into two parts, denoted respectively as unlabeled sample I and unlabeled sample II. Then a graph-all the labeled samples are used to train the multiple decision trees. If the classification result is consistent with the one of label prediction, then the unlabeled sample I is added to the labeled samples and used for building RF model. While, the data of unlabeled sample II are utilized for testing of the obtained RF model. Finally, the developed RF algorithm is applied to an experimental platform of rotating machinery. It is shown from the simulation results that, for the cases of noisy samples with unsatisfying labels, the new developed semi-supervised RF algorithm can improve the fault classification accuracy than the conventional RF.