Kernel extreme learning machine based hierarchical machine learning for multi-type and concurrent fault diagnosis

The detection and identification of faults in rotary machines are of great significance to the mechanical equipment reliability especially the gearbox. Traditional machine learning algorithms suffer from low diagnosis accuracy of faults that have multiple types and exist concurrently. A novel machine learning method called hierarchical machine learning (HML) was proposed in this study to improve the faults diagnosis accuracy. The proposed algorithm consists of two layers. The first layer comprises a traditional machine learning model to identify the faults with distinguishable features and filter out these faults with indistinguishable features. The second layer model recognizes the faults filtered out by the first layer. In order to verify the effectiveness of the proposed method, the gearbox simulation experiment is carried out in the study. The simulation results validate that the proposed method outperforms other algorithms under an identical measure.