Plastic gear remaining useful life prediction using artificial neural network

Prognostic and health management (PHM) of plastic gears has attracted attention due to an increasing performance of plastic gears, uncovering potential applications in the industry, especially in vehicle transmissions. Meanwhile, health indicator (HI) construction and remaining useful life (RUL) estimation are two key elements to efficiently perform PHM. In this paper, a health indicator generator (HIG) based on an artificial neural network (ANN) is constructed. The HIG is learned from training data extracted from plastic gears’ raw vibration data using the Fourier decomposition method (FDM) in a sensitive frequency band (SFB) and labeled using a change-point detection algorithm (CDA). Three prediction strategies, including linear regression (LR), estimation of parameters for Weibull distribution (EWD), HI combined average RUL (HI-ARUL), are deployed using HI generated from HIG to predict the RUL of the plastic gear. The results show that the generated HI is sensitive to the early failure of plastic gears and is capable of applying an efficient and precise diagnosis method, which can be performed during the whole working time of plastic gear with prediction errors smaller than 7%.