Data-Driven VICAR Modeling of Nonstationary Planetary Gearbox Vibration

Abstract

Planetary gearbox fault detection is important in terms of life-threatening failure prevention and maintenance optimization. This paper focuses on the representation of the planetary gearbox baseline vibration signals via time series models. Faults can be detected by examining any changes in model residuals or parameters. We propose a modified varying index coefficient autoregression (VICAR) model that effectively makes use of the rotating speed while retaining the highly flexible nonlinearity modeling capacity of the VICAR model. The modification lies in separating the lagged predictor and rotating speed via independent smooth functions. Parameter estimation and variable selection methods were developed accordingly. An experimental study was conducted which reveals the superiority of the modified VICAR model in comparison with expanded VICAR and linear parameter varying autoregression models.