Data fusion for automating condition monitoring of wooden railway sleepers

Abstract

Wooden railway sleeper inspections in Sweden are currently done by hand. That is to say, a human inspector in charge of the maintenance activities visually examines each structure in turn for the presence of cracks on the sleeper. Where necessary some deeper inspection may be carried out on site, for example using an axe to hit and judge the condition of the sleeper by listening to the sound produced. Though the manual procedure uses non-destructive testing methods (visual and sound analysis), decision-making is largely based on intuition; moreover the process is rather slow, expensive and also requires skilled and trained staff. Maintaining an even quality standard is another serious issue. Hence, it is desired to automate the human inspection process by proposing automatic testing procedures for future inspections concerning the condition of the sleeper. Studies based on emulation of the human inspection process have been considered a promising route of enquiry for automation. Such an emulation process is achieved by selecting and evaluating two non-destructive inspection methods. The first method (impact acoustic analysis) aims to build an automatic system to replace the usage of an axe for distinguishing sounds. The second method (visual analysis) is to develop an appropriate machine vision algorithm to replicate the visual examination. Further, the above-mentioned methods were fused (data fusion) to generate a single output condition concerning the condition of the sleeper. In the current work, fusion has been achieved in mainly three levels, namely sensor-level, feature-level and classifier-level. Experimental results achieved in this work indicate that data fusion has achieved superior performance when compared with using data from one method at a time.