Continuous Health Monitoring of Rolling Element Bearing Based on Nonlinear Oscillatory Sample Entropy

As a nonlinear measure, sample entropy (SE) can be considered a suitable parameter for characterizing rolling element bearing health status by measuring complexity of vibration signals. However, in continuous monitoring scenario under noisy condition, all components of a multicomponent bearing signal are not equally sensitive toward change of SE value. As a consequence, a direct application of SE results in inefficient early fault warning and inability to differentiate among different fault types. To deal with this problem, instead of direct utilization of a whole vibration signal, its principal component (PC) sensitive to SE calculation is separated with the help of continuously adjustable parameterized tunable
$Q$
factor wavelet transform (TQWT). Since TQWT uses an oscillation-based bearing PC separation scheme for SE calculation, the newly proposed measure is termed as oscillatory sample entropy (OSE). Due to the biasness of SE algorithm toward bearing PC, the proposed OSE can anticipate theoretical concept of complexity change more efficiently with the change of bearing health. Two experimental case studies have shown that proposed OSE can not only overcome the limitations of SE algorithm but also demonstrate superiority over approximate entropy (AE) and fuzzy entropy (FE) for continuous monitoring of bearing health.