Camacho Alcocer Track Data-Oriented Maintenance Intervention Limit Determination for Ballasted Light Rail Tracks through Multibody Simulations

1. Auflage 2021
ISBN: 978-3-7534-3403-2
Verlag: BoD - Books on Demand
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 208 Seiten

Light rail trains (LRT) are an important part of public transport but due to perceived high life-cycle costs are not always considered suitable. Life cycle cost reduction might be achieved through a knowledge-based maintenance management rather than just on experience. This work develops limits of maintenance and renewal of LRT systems based on vehicle reactions to the current track quality through measured data, multibody simulations and track geometry indices. An approach based on knowledge would lead to a track condition which allows a safe, comfortable, and under an appropriate maintenance strategy, economically profitable operation.

David Camacho Alcocer was born in Naucalpan, Mexico. He studied his Bachelor´s degree in Civil and Environmental Engineering at the University of Massachusetts, Amherst. In Los Angeles, California, he developed his engineering career as an civil engineer in medium and large land development projects. He studied his Master´s degree in Infrastructure Planning at the University of Stuttgart. In October 2019 obtained his Ph.D. at the Institute of Railway and Transportation Engineering of the University of Stuttgart.

Autoren/Hrsg.

Camacho Alcocer, David

Leseproben

List of Figures
Figure 1: Share of costs for the Life Cycle of an Urban Railway (own work per (Kochs and Marx 2009)) Figure 2: Central aspects in the infrastructure investment strategy (own work per (Tzanakakis 2013)) Figure 3: Conceptual diagram (higher level) of the study (own work) Figure 4: Working Definition of Track Quality (own work) Figure 5: Rail coordinate system (own work per (Andrea Haigermoser 2013)) Figure 6: Track coordinate system (own work per (Andrea Haigermoser 2013)) Figure 7: Wavelengths affecting riding quality (own work) Figure 8: Example of transferring function of chord measuring (chord division 4m/6m) (own work per (Deutsches Institut für Normung e. V. 2016)) Figure 9: Example of symmetrical chord system (own work) Figure 10: Example of signal distortion due to chord measurement per (Deutsches Institut für Normung e. V. 2016) Figure 11: Basicentric axes of the human body (own work per (International Organization for Standardization 1997)) Figure 12: Weighting curve Wb (z direction) and Wd (x,y direction) for mean comfort evaluation (own work per (The British Standards Institution 2009)) Figure 13: Schematics of maintenance intervention levels conceived for LRT systems (own work per (Kochs and Marx 2009)) Figure 14: Fractal plot example pattern of geometry deviation for the track geometry shown per (Hyslip 2002) Figure 15: Principle of superposition in LIT systems SYMISO from individual SYSISO per (Luber et al. 2010) Figure 16: Hamming window applied to signal to reduce spectral leakage (own work) Figure 17: FFT of signals depicting amplitude’s differences (own work) Figure 18: PSD smoothing using pwelch function with different number of segments (own work) Figure 19: PSD standard curves for vertical irregularities per ERRI B176 and FRA (own work) Figure 20: Chord measuring system - calculation of versine/sagitta per (Lewis 2011) Figure 21: Transfer function of the GT2 HuDe chord-system (own work) Figure 22: Stuttgart's Track Recording Vehicle - GT2 HuDe (photos: David Camacho) Figure 23: Angular measurements of an inertial measuring system (own work) Figure 24: Chord measuring system configuration per (Andrea Haigermoser 2013) Figure 25: Chord system measuring principle per (HuDe Mess- & Anlagentechnik GmbH 2011) Figure 26: Data treatment process for vertical irregularities (own work) Figure 27: Reconstruction of signals (own work) Figure 28: Data filtering in forward and reverse directions (own work) Figure 29: Vertical irregularity of worse day observed against to limits per (The British Standards Institution 2010) Figure 30: Moving SD of worse vertical track geometry observed against limit per (The British Standards Institution 2010) Figure 31: Lateral irregularity of worse day observed against limits (own work) Figure 32: Moving SD of worse lateral track geometry observed against limit (own work) Figure 33: Process for the calculation of PSD for vertical irregularities (own work) Figure 34: PSD of vertical irregularities for the worse, best and latest track geometry quality (own work) Figure 35: PSD of horizontal irregularities for the worse, best and latest track geometry quality (own work) Figure 36: PSD of gauge depicting deviation of curves due to lack of filtering (own work) Figure 37: PSD of gauge irregularities for the worse, best and latest track geometry quality (own work) Figure 38: PSD of cross level irregularities for the worse, best and latest track geometry quality (own work) Figure 39: Statistics of signals for Ns = 200 (own work) Figure 40: Signals generated through a Fourier trigonometric function and SIMPACK© (own work) Figure 41: Vertical vs horizontal SD for tracks 330i and 400i (own work) Figure 42: Cross section of typical concrete sleeper ballasted track (own work) Figure 43: Composition of total track modulus C per (Martin et al. 2016; Kerr 2002) Figure 44: Typical cross section of ballasted track for the system under study per (Benz 2016) Figure 45: Transition from a current empirical approach to conduct maintenance to a rational-knowledge-based approach per (Tzanakakis 2013).. Figure 46: Effects of segmentation on a track with known history (own work) Figure 47: SD development for tr 330 and tr 400 depicting the difference in geometry quality (own work) Figure 48: Improvement of standard deviations of a track’s parameter due to tamping per (Lichtberger 2005a) Figure 49: Principles of an integrated track maintenance per (Popovic et al. 2017) Figure 50: Global and local coordinate systems showing the relationship of two rigid bodies to one another and to a global reference adapted from (Rill and Schaeffer 2010) Figure 51: Tangential forces and moment developed at the contact patch adapted from (Rill and Schaeffer 2010) Figure 52: DT8.10 LRT vehicle used to model MBS (photo: author) Figure 53: DT8.10 SIMPACK© model (own work based on screenshot of MBS model) Figure 54: Schematic configuration of simulation model adapted from (Strobel etal.) Figure 55: SIMPACK© model of bogie and suspensions per (Skorsetz et al. September 23 -27) Figure 56: Comparison of MBS model and real train accelerations (own work) Figure 57: Accelerations measured by six different sensors spread in passenger cabin according to (The British Standards Institution 2009) (own work) Figure 58: RMS of signals measured by six different sensors within the passenger cabin per (The British Standards Institution 2009) (own work) Figure 59: Comfort level of signals measured by six different sensors within the passenger cabin calculated per (The British Standards Institution 2009) (own work) Figure 60: Process for excitation creation in SIMPACK© based on PSD functions of measured track irregularities (own work) Figure 61: Comparison of PSD of signal day 320 as measured and as obtained in SIMPACK© (own work) Figure 62: Comparison of signals generated in SIMPACK© through PSD functions of measured signals vs the signal measured in spatial domain (own work) Figure 63: Mean comfort index for all days plus day 320 with increased magnitudes (own work) Figure 64: Mean comfort index development for days after renewal (own work) Figure 65: SD of vertical (top) and horizontal (bottom) irregularities for track 330 (own work) Figure 66: Comparison of vertical irregularities produced through double integration versus measured (provided) and simulated vertical irregularities (own work) Figure 67: Comfort index for synthetic signals displaying higher track irregularities (own work) Figure 68: Magnitude increase based on track 400 to obtain refined limit values (own work) Figure 69: Comfort limit for “refined” magnitude increased signals (own work) Figure 70: PSD of limit values for vertical irregularities (own work) Figure 71: TGI representation for track 330 for a vehicle riding at 50 km/h (own work) Figure 72: TGI of track 400 (own work based on (Araji 2018)) Figure 73: Deterioration process of track 330 (own work) Figure 74: Deterioration process of track 400 (own work per (Araji 2018))... Figure 75: Track Geometry Parameters (own work per (Deutsches Institut für Normung e. V. 2016)) Figure 76: Mean comfort index NMV calculation process (own work) Figure 77: Weighting curves Wd and Wb (own work) Figure 78: Simulated axP and weighted axPWd accelerations in the x-direction (own work) Figure 79: Simulated ayP and weighted ayPWd accelerations in the y-direction (own work) Figure 80: Simulated azP and weighted azPWb accelerations in the z-direction (own work) Figure 81: RMS and 95th percentile of weighted acceleration axPVSWd in the x-direction (own work) Figure 82: RMS and 95th percentile of weighted acceleration ayP95Wd in the y-direction (own work) Figure 83: RMS and 95th percentile of weighted acceleration azP95Wb in the z-direction (own work) Figure 84: Statistics of signals for Ns = 50 (own work) Figure 85: Statistics of signals for Ns = 100 (own work) Figure 86: Statistics of signals for Ns = 300 (own work) Figure 87: Position of sensors in the cabin per (The British Standards Institution 2009) (own work based on image form (Bauer and Theurer 2000; Holzscheiter 2017)) Figure 88: Location of sensors at the axle box of wheels 15 and...