Pattern recognition based on statistical methods combined with machine learning in railway switches

Railway switches are critical components in the rail system. Operation and maintenance tasks are essential to ensure proper functioning and avoid any failure that can cause delays, reducing operational safety. Data from condition monitoring systems requires advanced analysis tools. This paper presents the analysis of power output data of railway switches. A novel approach is proposed based on statistical analysis techniques combined with Machine Learning techniques to classify power curves by analyzing different sections of the power curves. These curves are studied statistically to classify them into normal and non-normal curves. Then, a dataset is generated with normal and non-normal labelled curves. Shapelets and k-Nearest Neighbour classification algorithms are applied to these data with good results (accuracy, sensitivity and specificity above 88% in each case). As a further analysis, a second dataset with the sectioned curves is done to detect non-normal curves without analyzing the complete curve. For this case study, k-Nearest Neighbour algorithm is able to classify with higher accuracy on the last section of the curve.