Hybrid Feature Selection Framework for Bearing Fault Diagnosis Based on Wrapper-WPT

A framework aimed to improve the bearing-fault diagnosis accuracy using a hybrid feature-selection method based on Wrapper-WPT is proposed in this paper. In the first step, the envelope vibration signal of the roller bearing is provided to the Wrapper-WPT. There, it is initially decomposed into several sub-bands using Wavelet Packet Transform (WPT), and a set out of nineteen time and frequency domain features are individually extracted from each sub-band of the decomposed vibration signal forming a wide feature pool. In the following step, Wrapper-WPT constructs a final feature vector using the Boruta algorithm, which selects the most discriminant features from the wide feature pool based on the important metric obtained from the Random Forest classifier. Finally, Subspace k-NN is used to identify the health conditions of the bearing, thus forming a hybrid signal processing and machine learning-based model for bearing fault diagnosis. In comparison with other state-of-the-art methods, the proposed method showed higher classification performance on two different bearing-benchmark vibration datasets with variable operating conditions.

Automated summary

This paper proposes a framework aimed at improving the accuracy of bearing-fault diagnosis. The framework utilizes a hybrid feature-selection method based on Wrapper-WPT. It decomposes the vibration signal using Wavelet Packet Transform and extracts time and frequency domain features. The features are then selected using the Boruta algorithm, and a Subspace k-NN is used for bearing fault diagnosis. The proposed method shows higher classification performance compared to other state-of-the-art methods.