Performance Degradation Assessment for Coaxial Bearings Using Kernel JADE and Two-Class Model

Abstract

Vibration analysis is proved effective for bearing performance degradation assessment (PDA). In most of the current studies, vibration signals from one monitored bearing are collected and analyzed. However, in the real-industrial applications, bearings in pairs or even more are often mounted on the same shaft. Therefore, the collected vibration signal is actually a mixed signal from multiple bearings. In this study, our recently developed method named kernel joint approximate diagonalization of eigenmatrices (JADE) is investigated for PDA under this situation. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the proposed method could extract latent source features that are accurately reflecting the performance degradation from the mixed vibration signal. The method includes the following three steps: 1) statistical features are calculated and transformed into the high-dimension through nonlinear mapping. 2) Latent source features are obtained by exploiting the fourth-order moments. 3) The two-class model (SS) is proposed to assess the difference between the monitored sample and the healthy sample, which could reflect the bearing health condition. The effectiveness of this method is verified using two experimental cases within four coaxial bearings. Comparative results show that the proposed method could identify the defect earlier than the traditional rms feature and the traditional JADE method and the principal component analysis method.