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This article presents a data-driven method of pattern identification for in-situ monitoring of fatigue damage in polycrystalline alloys that are commonly used in aerospace structures. The concept is built upon analytic signal space partitioning of ultrasonic data sequences for symbolic dynamic filtering of the underlying information. The statistical patterns of evolving damage are generated for real-time monitoring of the possible structural degradation under fatigue load. The proposed method is capable of detecting small anomalies (i.e. deviations from the nominal condition) in the material microstructure and thereby generating early warnings on damage initiation. The damage monitoring algorithm has been validated on time series data of ultrasonic sensors from a fatigue test apparatus, where the behavioural pattern changes accrue because of the evolving fatigue damage in polycrystalline alloys.