Rail defect diagnosis using wavelet packet decomposition

One of the basic tasks in railway maintenance is inspection of the rail in order to detect defects. Rail defects have different properties and are divided into various categories with regard to the type and position of defects on the rail. This paper presents an approach for the detection of defects in rail based on wavelet transformation. Multi-resolution signal decomposition based on wavelet transform or wavelet packet provides a set of decomposed signals at distinct frequency bands, which contains independent dynamic information due to the orthogonality of wavelet functions. Wavelet transform and wavelet packet in tandem with various signal processing methods, such as autoregressive spectrum, energy monitoring, fractal dimension, etc., can produce desirable results for condition monitoring and fault diagnosis. Defect detection is based on decomposition of the signal acquired by means of magnetic coil and Hall sensors from the railroad rail, and then applying wavelet coefficients to the extracted signals. Comparing these extracted coefficients provides an indication of the healthy rail from the defective rail. Experimental results are presented for a healthy rail and some of the more common defects. Deviation of wavelet coefficients in the healthy rail case from the case with defects shows that it is possible to classify healthy rails from defective ones.