This paper presents a novel technique that utilizes wavelet analysis to identify and predict the defects in railroad foundations and rails to prevent derailment or other damages. The proposed defect detection algorithm eliminates the use of wheel and/or track monitoring systems, which are expensive and time inefficient. The algorithm has been validated for the rail crack prediction using only vertical accelerometer signal which accurately detects impending rail breakage while distinguishing the signal generated by special track components such as rail joins and switches. Since the algorithm is flexible, further development can be tailored to detect significantly different rail defects such as track shift and other rail foundation defects. The algorithm is further improved by incorporating SIMPACK dynamic simulation to assist classification of the acceleration signatures. The actual data was then compared to simulation in order to validate the effectiveness of the algorithm.

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