Sparse linear parameter-varying autoregressive moving average modelling of non-stationary gearbox vibration signals

Abstract

Abstract Gearboxes frequently operate under variable speed conditions, leading to the non-stationarity of collected monitoring signals. This paper proposes a sparse linear parameter-varying autoregressive moving average (Spa LPV-ARMA) model for fault detection of gearboxes under variable speed conditions. The Spa LPV-ARMA model integrates the advantages of LPV concepts, sparse models, and ARMA models. The proposed model has a more compact structure compared to the reported Spa LPV-AR model, which results in a lower ratio of parameter number to data amount, and the compact model structure also allows the Spa LPV-ARMA model to have higher computational efficiency during the testing phase. A simulation study was conducted to validate the performance of the proposed method. The results demonstrate that the Spa LPV-ARMA model exhibits superior modeling accuracy compared to the reported Spa LPV-AR model. Additionally, the fault detection method based on the Spa LPV-ARMA model achieves a higher fault detection rate when contrasted with a model that solely considers the autoregressive component.