Remaining Useful Life Prediction for Nonlinear Degrading Equipment With Quantization Errors

Remote sensing, as a significant technique for acquiring the condition monitoring information for remaining useful life (RUL) prediction of equipment, primarily consists of two typical steps: sensor monitoring and analog-to-digital conversion (ADC). These two steps may inevitably introduce errors owing to measurement noise and quantization. Unfortunately, existing RUL prediction methods focus on the measurement errors from sensor monitoring, but ignore quantization errors from ADC. In this study, we present a nonlinear degradation model that incorporates mixed random noise to estimate the RUL of the stochastic degrading equipment based on observed degradation data involving quantization errors. Through a diffusion process with a nonlinear drift coefficient, the distribution of RUL prediction considering measurement errors and quantization errors is derived. Subsequently, we employ the particle filter to estimate the degradation state based on the observed degradation data, which is affected by quantization error, measurement error, and strong nonlinearity. Additionally, the particle-expectation-maximization algorithm is utilized to identify and update model parameters. Finally, numerical simulations verify the effectiveness of our proposed model, and experimental verification is conducted using degradation data from control moment gyroscopes. In both cases, the R-Square value of the proposed method is no less than 0.98, indicating superior prediction performance compared to the linear method.