This paper presents real-time detection of fatigue damage in mechanical structures using ultrasonic sensing methodology. The data-driven pattern identification method for anomaly detection is based on the tools derived from Statistical Mechanics and Symbolic Dynamics. The concept of Escort Distributions has been used to identify the behavioral patterns changes in complex systems due to gradual evolution of anomalies. The real-time information of evolving fatigue damage provides early warnings of forthcoming catastrophic failures. The anomaly detection method has been experimentally validated on poly-crystalline alloys using ultrasonic data generated from a fatigue damage testing apparatus.