Digital signal processing for rail monitoring by means of ultrasonic guided waves

Recent train accidents have reaffirmed the need for developing rail defect detection systems that are more effective than those used today. One of the recent developments in rail inspection is the use of ultrasonic guided waves (UGWs) and non-contact probing techniques to target transverse-type defects. Besides the obvious advantages of non-contact probing, that include robustness and a potential for large inspection speed, such a system can theoretically detect transverse defects under horizontal shelling or head checks. This paper demonstrates the effectiveness of digital signal processing to enhance the damage detection sensitivity of the non-contact system. The method proposed here combines the advantages of UGW inspection with the outcomes of the Discrete Wavelet Transform (DWT) that is used for extracting robust defect-sensitive features. In particular, the DWT is exploited to de-noise the ultrasonic signals in real-time and generate a set of relevant wavelet coefficients to construct a uni-dimensional damage index. The general framework proposed in this paper is applied to the detection of crack-like and internal defects in a railroad track mock-up built at UCSD. The proposed signal analysis approach is general and is applicable to many other structural monitoring applications using UGWs as the main defect diagnosis tool.