Predicting Remaining Useful Life Using Continuous Wavelet Transform Integrated Discrete Teager Energy Operator with Degradation Model

Abstract

Prognostics health management (PHM) for rotating machinery has become an important process for increasing reliability and reducing machine malfunctions in industry. Bearings are one of the most important equipment parts and are also one of the most common failure points. To assess the degradation of a machine, this paper presents a bearing remaining useful life (RUL) prediction method. The method relies on a novel health indicator and an exponential degradation model to predict bearing RUL. The health indicator is extracted by using a continuous wavelet transform spectrogram integrated discrete Teager energy operator to process horizontal vibration signals obtained from bearings. We present an exponential degradation model to estimate RUL using this health indicator. In the training phase, the degradation detection threshold and the failure threshold of this model are estimated by kernel density estimation of 100 bootstrapped samples. These bootstrapped samples are taken from the six training sets. In the test phase, the health indicator and the model are used to estimate the bearing’s current health state and predict the RUL. This method is suitable for evaluating the degradation of bearings. The experimental results show that this method can effectively monitor bearing degradation and predict the RUL.