An empirical mode decomposition-driven machine learning framework for vibration-based defect diagnosis of bevel gearboxes

Abstract

Gearboxes are widely used in industries that involve rotating elements, owing to their ability to transmit power and velocity; hence, their failures significantly impact machine performance. As a result, numerous studies focus on fault identification and gearbox classification. The present study developed a Machine fault simulator (MFS). It diagnosed faults in the bevel gearbox using vibration signals via empirical mode decomposition (EMD) and machine learning (ML) algorithm. The ML methods considered here are Random Forest (RF) and Artificial Neural Networks (ANN). The vibration signals were collected using an accelerometer under Normal, Tooth breakage, Worn-out and Tooth crack gear fault conditions. Using EMD, vibration signals are decomposed into a limited number of intrinsic mode functions (IMFs). Various statistical features of the decomposed EMD signals are extracted to obtain the input vector. The decision tree (J-48) algorithm is used to identify key features for classifier training and testing. The experiments are performed on healthy and simulated-fault gears under 30 N load and 750 r/min conditions. The relative effectiveness of RF and ANN classifiers is also compared through the test part. The experimental results show that the proposed RF algorithm gives good classification accuracy. Further, classification accuracy improves when using RF along with EMD. The proposed methodology will aid practitioners and researchers in diagnosis faults in rotating machine elements.