Remain useful life prediction of rolling bearings based on exponential model optimized by gradient method

Remaining useful life (RUL) using exponential model (EM) prediction has been a hot research topic in the construction of prognostics health management (PHM) systems. However, in RUL prediction of rolling bearings, the EM 1) depends on the appropriate first prediction time (FPT), 2) requires reliable methods to optimize the model. Therefore, an improved EM is developed to predict the RUL of rolling bearings. Firstly, an adaptive method based on kurtosis and root mean square (RMS) of bearing vibration signals is used to determine the appropriate FPT. Secondly, gradient descent method is used to reliably optimize the EM. A commonly used bearing degradation datasets are analyzed to show the advantages of the present method. Compared with the traditional EM, the method can not only adaptively determine FPT, but also predict RUL more accurately.

Automated summary

The improved exponential model (EM) is developed to predict the remaining useful life (RUL) of rolling bearings, by determining the appropriate first prediction time (FPT) using an adaptive method based on kurtosis and root mean square (RMS) of bearing vibration signals, and optimizing the model reliably through gradient descent method. Compared with the traditional EM, this method can predict RUL more accurately.