Fault Feature Extraction of Gearbox Based on Kurtosis-Weighted Singular Values

Abstract

As one of the most important components in rotating machinery, gearbox is fragile due to its harsh working environment. A new feature extraction method for gearbox fault diagnosis integrating singular value decomposition (SVD) with multidimensional scaling (MDS) is proposed in this article. SVD is used to extract the singular components (SCs), which reflect the main energy of the vibration signal, and the kurtosis values of SCs are calculated to detect the fault sensitivity of each singular value. The kurtosis-weighted singular values are applied to classify different conditions of gearbox supplemented by the MDS and fuzzy C-means clustering (FCM) method. Five gearbox operating conditions including tooth breakage, tooth pitting, gear eccentricity and normal are simulated to test the performance of proposed feature extraction method in an experiment rig. Partition coefficient (PC) and partition entropy (PE) are used to evaluate the classification effect of weighted singular values. The result suggests that the proposed kurtosis-weighted singular values perform better in distinguishing the different conditions of gearbox than original singular values.