Condition monitoring of wooden railway sleepers using time-frequency techniques and pattern classification

Railway sleepers are a key engineering element of all railways. Lack of much sophistication in monitoring railway sleepers makes it a key problem within the rail transportation domain. Current day condition monitoring applications involving wooden railway sleepers are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. Decision making is largely based on intuition; moreover the process of manually inspecting sleepers is rather slow and expensive. Maintaining an even quality standard is another serious issue. In this article, a pattern recognition and classification approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. Features were extracted from the impact acoustic emissions of wooden sleepers and were used for pattern classification. Time-frequency based feature extraction techniques such short-time Fourier transform and discrete wavelet transform yielded good results. Multi-layer perceptron, radial basis function neural networks and support vector machine classifiers have been tested and compared. Further classifier fusion was investigated by considering the output of single best classifiers as input to a new classifier with an aim of improving performance. Results obtained experimentally demonstrate a classification accuracy of around 84%.