Fault diagnosis of tractor auxiliary gearbox using vibration analysis and random forest classifier

Abstract Accurate detection of mechanical components faults is an essential step for reduction of repair cost, human injury probability and loss of production. Using intelligent fault diagnosis systems in tractor could prevent secondary damage, thereby avoiding heavy consequences. In this study, fault diagnosis of tractor auxiliary gearbox is presented. Vibration signals of healthy and faulty pinions gear under three different operational conditions (Rotational speeds of 600 RPM, 1350 RPM and 2000 RPM) were collected, and discrete wavelet transform (DWT) was used as signal processing. Useful statistical features were calculated from collected signals. Correlation-based feature selection (CFS) method was used to find the best features. Random forest (RF) and multilayer perceptron (MLP) neural networks were employed to classify the data. The overall accuracy of RF classifier without using feature selection were 86.25%, at 600 RPM. The corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%. The best results obtained at 1350 RPM, since the detection accuracy was 95%. The results of this study demonstrated the effectiveness and feasibility of the proposed method for fault diagnosis of tractor auxiliary gearbox.