Improved phase space warping method for degradation tracking of rotating machinery under variable working conditions

Abstract

Abstract Health Indicators (HI) that can effectively track the degradation state and are insensitive to the change of working conditions are of great significance to the process of Prognostic and Health Management (PHM). Based on the rapidly changing one-dimensional vibration signal to reconstruct the phase space, the Phase Space Warping (PSW) method was used to quantify the small curvature of the phase space trajectory caused by the damage, and then the HI that can represent the evolution process of the slow-changing damage could be obtained with the curvature. However, there are still several problems with the PSW method that need to be solved urgently. Firstly, it does not provide clear optimization criteria and adaptive determination strategies for the two key parameters of phase space reconstruction, which are, time delay and embedded dimension. Secondly, the HI extracted based on the PSW is greatly affected by noise and lacks a proper feature fusion strategy. Aiming at these problems, the Improved Phase Space Warping (IPSW) method is proposed in this paper. The contribution of IPSW is as follows: (1) IPSW considers the degree of comprehensive mutual information between multi-dimensional components, and uses comprehensive mutual information entropy as an optimization index, and gives clear guidelines on how to select optimal parameters; (2) IPSW introduces the correlation analysis method to fuse the acquired multi-modal trend components and then obtains the HI that can characterize the damage degradation trend. The simulation and experimental signal verification results of bearing fault degradation under the condition of variable speed show that the HI extracted based on the IPSW method can reduce the influence of variable speed and track the bearing damage degradation trend effectively.