Concurrent analysis of static deviation and dynamic oscillation for momentum wheel bearing health monitoring and prognostication

Abstract

Momentum wheel bearing is a critical component within satellite systems, and its condition monitoring not only extends the operational lifespan of the satellite but also ensures the seamless fulfillment of its mission objectives. Various data-driven techniques have been introduced to assimilate health-related information. However, these techniques neglect the significant challenges posed by robust disturbance and volatility of degradation process, resulting in suboptimal evaluation performance. To address these issues comprehensively, this paper proposes a novel approach named canonical variable fluctuation analysis (CVFA) to facilitate precise health monitoring of momentum wheel bearings by concurrent analysis of static deviation and dynamic oscillation. Firstly, three quantifiable standards of consistency, accuracy and sensitivity are defined to select the degradation trend-related indices from multi-domain features, which provides an automatic and objective feature selection method. Subsequently, CVFA is developed to realize feature reduction and extracts the dynamic information from the features with strong disturbance and high fluctuation. Two Fluctuation (F) statistics are defined to characterize the health degradation trend by integrating both static deviation and dynamic volatility within a sliding window. Afterwards, autoregressive moving average (ARMA) model is constructed on the basis of F statistics for short-term prognostication, which enables proactive detection of degradation trends. Lastly, by integrating two F statistics, a health degree (HD), which is independent of parameter adjustments, is defined to intuitively represent bearing health status. The efficacy and superiority of the proposed method are substantiated through validation and analysis conducted using accelerated life tests of bearings.