Abstract
This work introduces a novel algorithm for the reconstruction of rolling stocks from a sequence of images. The research aims at producing an accurate and wide image model that can be used as a Digital Twin (DT) for diagnosis, fault prediction, maintenance, and other monitoring operations. When observing large surfaces with nearly constant textures, metallic reflections, and repetitive patterns, motion estimation algorithms based on whole image error minimization and feature pairing with Random Sampling and Consensus(RANSAC) or Least Median of Squares (LMedS) fail to provide appropriate associations. To overcome such an issue, we propose a custom Kalman Filter (KF) modified by adding multiple input-noise sources represented as a Gaussian mixture distribution (GM), and specific algorithms to select appropriate data and variance to use for state prediction and correction. The proposed algorithm has been tested on images of train vessels, having a high number of windows, and large metallic paintings with constant or repetitive patterns. The approach here presented showed to be robust in the presence of high environmental disturbances and a reduced number of features. A large set of rolling stocks has been collected during a six months campaign. The set was employed to demonstrate the validity of the proposed algorithm by comparing the reconstructed twin versus known data. The system showed an overall accuracy in length estimation above 99%.
Highlights
E VEN if the national report on railway security [1], assesses Italy as one of the safest railways in Europe, in 2018 the average of significant railway accidents was one every 3.3Mln Tr-km (Million of Train-kilometers), and the average number of deaths in train accidents was one over 5.133Mln Tr-km
When the largest percentage of matches corresponds to good features, whichever algorithm is used, it leads to the same good result
When rebuilding a complete Digital Twin (DT), even a few misaligned frames lead to an inappropriate reconstruction of the whole train
Summary
E VEN if the national report on railway security [1], assesses Italy as one of the safest railways in Europe, in 2018 the average of significant railway accidents was one every 3.3Mln Tr-km (Million of Train-kilometers), and the average number of deaths in train accidents was one over 5.133Mln Tr-km. Train maintenance is performed in two ways [2]: corrective maintenance and predictive maintenance. The goal is to move from the actual plan-based to condition-based maintenance that uses monitoring tools to assess the health status of the train. An early example was the optical flow estimation, proposed by Lucas and Kanade [23]. Their approach detects object motion between frames by computing the relative spatial gradient. This method works on a local neighborhood of the moving object and successfully recovers the motion when an object moves slowly and shows a pattern that is not uniform and distinguishable from the background. The technique becomes unstable in the presence of constant or repetitive patterns, rapid motions and/or background with similar patterns
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