Fault Diagnosis of a Rolling Bearing Using Wavelet Packet Denoising and Random Forests

The faults of rolling bearings can result in the deterioration of rotating machine operating conditions, how to extract the fault feature parameters and identify the fault of the rolling bearing has become a key issue for ensuring the safe operation of modern rotating machineries. This paper proposes a novel hybrid approach of a random forests classifier for the fault diagnosis in rolling bearings. The fault feature parameters are extracted by applying the wavelet packet decomposition, and the best set of mother wavelets for the signal pre-processing is identified by the values of signal-to-noise ratio and mean square error. Then, the mutual dimensionless index is first used as the input feature for the classification problem. In this way, the best features of the five mutual dimensionless indices for the fault diagnosis are selected through the internal voting of the random forests classifier. The approach is tested on simulation and practical bearing vibration signals by considering several fault classes. The comparative experiment results show that the proposed method reached 88.23% in classification accuracy, and high efficiency and robustness in the models.