Browsing by Author "Kluge, Franziska"

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    Common Crossing Structural Health Analysis with Track-Side Monitoring
    (EDIS, University of Zilina, Slovakia, 2019) Sysyn, Mykola P.; Nabochenko, Olga S.; Kluge, Franziska; Kovalchuk, Vitalii V.; Pentsak, Andriy
    EN: Track-side inertial measurements on common crossings are the object of the present study. The paper deals with the problem of mea- surement's interpretation for the estimation of the crossing structural health. The problem is manifested by the weak relation of measured acceleration components and impact lateral distribution to the lifecycle of common crossing rolling surface. The popular signal processing and machine learning methods are explored to solve the problem. The Hilbert-Huang Transform (HHT) method is used to extract the time-frequency features of acceleration components. The method is based on Ensemble Empirical Mode Decomposition (EEMD) that is advantageous to the conventional spectral analysis methods with higher frequency resolution and managing nonstationary nonlinear signals. Linear regression and Gaussian Process Regression are used to fuse the extracted features in one structural health (SH) indicator and study its relation to the crossing lifetime. The results have shown the significant relation of the derived with GPR indicator to the lifetime.
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    Prediction of Rail Contact Fatigue on Crossings Using Image Processing and Machine Learning Methods
    (Springer Verlag, Germany, 2019) Sysyn, Mykola P.; Gerber, Ulf; Nabochenko, Olga S.; Gruen, Dmitri; Kluge, Franziska
    EN: Abstract In this paper, an application of computer vision and machine learning algorithms for common crossing frog diagnostics is presented. The rolling surface fatigue of frogs along the crossing lifecycle is analysed. The research is based on information from high-resolution optical images of the frog rolling surface and images from magnetic particle inspection. Image processing methods are used to preprocess the images and to detect the feature set that corresponds to objects similar to surface cracks. Machine learning methods are used for the analysis of crack images from the beginning to the end of the crossing lifecycle. Statistically significant crack features and their combinations that depict the surface fatigue state are found. The research result consists of the early prediction of rail contact fatigue.
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    Turnout Remaining Useful Life Prognosis by Means of On-Board Inertial Measurements on Operational Trains
    (Taylor & Francis, 2020) Sysyn, Mykola P.; Gerber, Ulf; Kluge, Franziska; Nabochenko, Olga S.; Kovalchuk, Vitalii V.
    EN: The paper deals with remaining useful life (RUL) prognosis of common crossings based on inertial measurements. Axle-box inertial measurements on operational trains could be a cheap alternative to conventional inspection means. The low correlation between maximal wheel acceleration and useful life of the crossing is considered and the reasons are analysed. A machine learning approach, including feature extraction, selection, fusion and degradation modelling, is then used to cope with the problem. More time domain and spectral features are extracted from measured vertical accelerations to provide a higher utilization of the available information. After removing redundant features, the data is fused using principal component analysis to obtain a condition indicator for common crossings. A data-driven prognostic methodology is proposed based on an iteratively updated exponential degradation model. The assessment of the prognosis quality is carried out depending on the crossing lifetime and the reached value of the condition indicator.