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In this paper, a model-based fault prognosis method is developed for hybrid systems with multiple incipient faults. The concept of augmented global analytical redundancy relations is proposed for the identification of degradation of components, such as sensors and actuators, which cannot be described by physical parameters. In addition, multiple incipient faults are considered in a complex hybrid system, and these faults can develop during a mode when the faults are not detectable. The unknown degradation characteristic of each incipient fault is identified with the closest matching one of some prescribed dynamic models. The resultant degradation model will serve as a base for prognosis. In the process of fault detection and isolation, the degradation models and faults are identified using a multiple-adaptive-hybrid-particle-swarm-optimization algorithm. The proposed methodology and algorithm are verified with simulation as well as experiments.