To address the limitations of existing real-time fault prediction methods for transformer winding insulation regarding the consideration of degradation path variations and the determinacy of fault thresholds, this study proposes a fault prediction method that integrates multi-source monitoring data and adaptive fault thresholds, accounting for the influences of different operational conditions and manufacturing processes on the degradation path and fault threshold of winding insulation. First, this method synthesizes the cumulative damage mechanisms caused by electrical, thermal, mechanical, and other stresses to generate a lightweight comprehensive degradation index through low-rank tensor fusion, which efficiently fuses multi-source data such as voltage and current. Second, considering the impact of varying operational conditions on the degradation process, functional time registration is employed to align the degradation time series of different devices. By applying nonlinear time transformation, physical time is mapped to degradation time that reflects the insulation degradation process, effectively eliminating temporal drift. Functional principal component analysis is then utilized to extract common degradation trends and individual variation characteristics from the aligned time series data, thereby establishing a data-driven degradation prediction model. Subsequently, Bayesian dynamic updating of principal component scores is introduced to continuously revise the posterior distribution using prior knowledge and real-time monitoring information, enabling personalized real-time prediction of degradation trends with reduced errors. Finally, to account for the uncertainty of fault thresholds resulting from differences in manufacturing processes, an adaptive threshold modeling method based on dynamic time warping (DTW) distance is proposed. By measuring the morphological similarity of degradation curves, an adaptive fault threshold model is constructed to predict the confidence interval of fault occurrence time. The results demonstrate that the comprehensive degradation index exhibits high similarity to the standardized furfural indicator, and the proposed method accurately predicts the fault time of winding insulation, with actual fault occurrence times consistently falling within the confidence intervals of the predicted fault times.