Browsing by Author "Nabochenko, Olga"
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Item Common Crossing Condition Monitoring with on-Board Inertial Measurements(Czech Technical University, Prague, 2019) Sysyn, Mykola; Nabochenko, Olga; Gerber, Ulf; Kovalchuk, Vitalii; Petrenko, OleksiyEN: A railway turnout is an element of the railway infrastructure that influences the reliability of a railway traffic operation the most. The growing necessity for the reliability and availability in the railway transportation promotes a wide use of condition monitoring systems. These systems are typically based on the measurement of the dynamic response during operation. The inertial dynamic response measurement with on-board systems is the simplest and reliable way of monitoring the railway infrastructure. However, the new possibilities of condition monitoring are faced with new challenges of the measured information utilization. The paper deals with the condition monitoring of the most critical part of turnouts - the common crossing. The application of an on-board inertial measurement system ESAH-F for a crossing condition monitoring is presented and explained. The inertial measurements are characterized with the low correlation of maximal vertical accelerations to the lifetime. The data mining approach is used to recover the latent relations in the measurement’s information. An additional time domain and spectral feature sets are extracted from axle-box acceleration signals. The popular spectral kurtosis features are used additionally to the wavelet ones. The feature monotonicity ranking is carried out to select the most suited features for the condition indicator. The most significant features are fused in a one condition indicator with a principal component analysis. The proposed condition indicator delivers an almost two-time higher correlation to the lifetime as the maximal vertical accelerations. The regression analysis of the indicator to the lifetime with an exponential fit proves its good applicability for the crossing residual useful life prognosis.Item Common Crossing Fault Prediction with Track Based Inertial Measurements: Statistical vs. Mechanical Approach(Akadémiai Kiadó, Hungary, 2019) Sysyn, Mykola; Gerber, Ulf; Nabochenko, Olga; Kovalchuk, VitaliiEN: The analysis of track based inertial measurements for common crossing fault detection and prediction is presented in the paper. The measurement of spatial acceleration in common crossing spike and impact position during overall lifecycle are studied regarding to rolling surface fatigue degradation. Two approaches for retrieving the relation of inertial parameters to common crossing lifetime are proposed. The first one is based on the statistical learning method - t-SNE algorithm that helps to find out similarities in measured dataset. The second one is a mechanical approach that handles the data with a fatigue and contact models. Both approaches allow the significant improvement of the common crossing fault detection as well as its early prediction.Item Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach(MDPI, 2021) Sysyn, Mykola; Przybylowicz, Michal; Nabochenko, Olga; Kou, LeiEN: The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement. View Full-TextItem Improvement of Inspection System for Common Crossings by Track Side Monitoring and Prognostics(Techno-Press, South Korea, 2020) Sysyn, Mykola; Nabochenko, Olga; Kovalchuk, Vitalii; Gruen, Dimitri; Pentsak, AndriyEN: Scheduled inspections of common crossings are one of the main cost drivers of railway maintenance. Prognostics and health management (PHM) approach and modern monitoring means offer many possibilities in the optimization of inspections and maintenance. The present paper deals with data driven prognosis of the common crossing remaining useful life (RUL) that is based on an inertial monitoring system. The problem of scheduled inspections system for common crossings is outlined and analysed. The proposed analysis of inertial signals with the maximal overlap discrete wavelet packet transform (MODWPT) and Shannon entropy (SE) estimates enable to extract the spectral features. The relevant features for the acceleration components are selected with application of Lasso (Least absolute shrinkage and selection operator) regularization. The features are fused with time domain information about the longitudinal position of wheels impact and train velocities by multivariate regression. The fused structural health (SH) indicator has a significant correlation to the lifetime of crossing. The RUL prognosis is performed on the linear degradation stochastic model with recursive Bayesian update. Prognosis testing metrics show the promising results for common crossing inspection scheduling improvement.Item Turnout Monitoring with Vehicle Based Inertial Measurements of Operational Trains: A Machine Learning Approach(University of Žilina, Slovakia, 2019) Sysyn, Mykola; Gruen, Dimitri; Gerber, Ulf; Nabochenko, Olga; Kovalchuk, VitaliiEN: A machine learning approach for the recent detection of crossing faults is presented in the paper. The basis for the research are the data of the axle box inertial measurements on operational trains with the system ESAH-F. Within the machine learning approach the signal processing methods, as well as data reduction classification methods, are used. The wavelet analysis is applied to detect the spectral features at measured signals. The simple filter approach and sequential feature selection is used to find the most significant features and train the classification model. The validation and error estimates are presented and its relation to the number of selected features is analysed, as well.