A Novel k-Nearest Neighbor Method with Distribution Discrepancy and Differential Feature Importance for Rolling...

Rolling bearings are among the most vulnerable components in various types of rotary machinery, and accurate fault detection and localization are essential. When a rolling bearing fails, the signal is non-stationary, and the energy distribution of the vibration signal varies depending on the fault location. In traditional k-nearest neighbor (KNN) fault diagnosis algorithms, Euclidean distance is primarily used to measure the distance between sample points, which is not effective at capturing similarity across different spatial distributions. Moreover, these algorithms assume equal feature importance, which does not reflect the actual characteristics of fault vibration signals. This study proposes a KNN-based rolling bearing fault diagnosis method that incorporates distribution discrepancy and differential feature importance. First, vibration signals are decomposed using three-level wavelet packet decomposition, and the energy of each node at the third level is used as the fault feature. Then, the mean impact value (MIV) algorithm is used to determine the relative importance of each feature, and the Earth mover's distance (EMD) is applied to measure differences between spatial distributions. By integrating Euclidean distance with MIV and EMD and applying the KNN majority voting rule, fault diagnosis is performed. The experimental results indicate that this method achieves a diagnostic accuracy of 99.43%, representing a 5.97% improvement compared to traditional KNN methods. The proposed method demonstrates accurate and effective fault diagnosis performance on the rolling bearing datasets used in this study.