Predictive maintenance has raised much research interest to improve the system reliability of a wind turbine. This paper presents a new model based approach of integrated fault diagnosis and prognosis for wind turbine remaining useful life estimation, especially the cases with limited degradation data. Firstly, a wavelet transform based fault diagnosis method is investigated to analyze the bearing incipient defect signatures, and the extracted features are then fused by the Health Index algorithm to represent the bearing defect conditions. Taking the empirical physical knowledge and statistical model in a Bayesian framework, the bearing remaining useful life prediction with uncertainty quantification is achieved by particle filter in a recursive manner. The integrated fault diagnosis and prognosis approach is validated using bearing lifetime test data acquired from a wind turbine in field, and the performance comparison with typical data driven technique outlines the significance of the presented method. (C) 2019 Elsevier Ltd. All rights reserved.