Bearing Degradation State Estimation Based on EEMD Improved Alignment Entropy and Correlation Analysis

In order to solve the problem of non-stationarity, nonlinearity, non-Gaussian and other characteristics of the vibration signal of rolling bearings and the low sensitivity of the arrangement entropy to extract the performance degradation characteristics of rolling bearings, a bearing degradation state estimation based on EEMD improved permutation entropy (IPE) and correlation analysis was proposed. Firstly, adaptive noise complete set of empirical mode decomposition algorithm vibration signals were decomposed eigen mode function, choice the IMF component and component of the original signal correlation signal reconstruction. Then, the degradation characteristics are extracted by the improved arrangement entropy, and the relationship between the extracted degradation characteristics and reliability of the extracted rolling bearings is evaluated by correlation analysis, so as to achieve the purpose of evaluating the degradation characteristics of bearings from the evolution law of bearing reliability. In this paper, the whole life data of bearings from Xi 'an Jiaotong University is used to verify the method. Compared with the method of permutation entropy, permutation entropy, etc., this method can estimate the degradation state of bearing effectively.