The present fault early-warning models for hydropower equipments are mostly small and scenario-customized models with low reusability, which places a significant burden on model development, operations and maintenance. A hydropower equipment fault early warning method based on time series foundation models is proposed, aiming at a general early warning goal of one model for multiple scenarios. To address the dilemma that time series foundation models struggle to effectively distinguish between normal and abnormal data due to their strong cross-distribution generalization ability, a similarity-constrained framework based on latent similarity-based modeling is novelly developed for time series foundation models. The framework constructs similarity-based constraints between the representations of test data and prototypes, thus limiting the model’s performance for abnormal data. The theoretical analysis results and several real-world equipment fault case analysis results from a large hydropower station in the Yangtze River basin indicate that, the framework is capable of enlargeing the prediction error gap between normal and abnormal data, improving the identifiability of abnormal patterns, and improving accuracy and earliness of early warning. The framework provides a practical approach for application of time series foundation models for general hydropower equipment early warning.