Sparse linear parameter varying autoregressive moving average (Spa LPV-ARMA) model is a powerful tool for dealing with non-stationary time series, and good fitting results can be achieved through the basis function expansion method, where parameters of the model are associated with additional variables. This paper proposes a novel enhanced Spa LPV-ARMA model with ensemble basis for gearbox fault detection. The proposed model incorporates the concept of ensemble learning by combining models with different basis functions, and a stepwise approach is utilized for model selection. The rational choice of the combination scale allows the ensemble model to have fewer parameters with higher accuracy. Simulation study is conducted to verify that the proposed ensemble basis Spa LPV-ARMA model exhibits higher modeling accuracy and fault detection performance.