A time series model-based method for gear tooth crack detection and severity assessment under random speed variation
Yuejian Chen, Stephan Schmidt, P. Stephan Heyns, Ming J. Zuo
Abstract
In industry (e.g., wind power), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults and assess their severity using vibration signals collected under different speed profiles. A few studies have been reported for condition monitoring of gearboxes under random speed variations, including a novelty diagnostic method and a support vector machine (SVM) based method. However, these methods either are based on the strict assumption that the rotating speed does not vary significantly within a rotating cycle or have the drawback of low classification accuracy. This paper presents a time series model-based method for gear tooth crack detection and severity assessment under random speed variation. Specifically, the rotating speed and phase are considered as covariates in a linear parameter varying autoregression (AR) model for representing impulsive vibration signals. We propose refined B-splines for mapping the dependency between AR coefficients and the rotating phase. The performance of the presented time series model-based method has been validated using laboratory signals. The presented method can assess 93.8% of the tooth crack severity state correctly, which is better than the novelty diagnostic method (74.4%) and SVM-based method (87.7%).