Estimating Vertical Profile Irregularities From Vehicle Dynamics Measurements

Track geometry is an important parameter, used by railways, for routine track maintenance. Laser based Track Recording Coaches (LTRCs) are widely availed by railways, for recording track geometry defects, such as: vertical profile, alignment, gauge etc. In this paper, a novel method for monitoring vertical profile irregularities by in-service rail vehicle dynamics measurements has been proposed. The procedure takes as input, data from bogie installed inertial measurement unit (IMU) measuring vertical acceleration and pitch-rate, and encoder measuring longitudinal velocity. Extended Kalman Filter (EKF) with Rauch-Tung-Striebel (RTS) smoothing and Extended Kalman Particle Filter (EPF) are reviewed, for detecting vertical profile irregularities. Process model of nonlinear state estimation filters, EKF and EPF, has been developed by applying a novel analytical technique that approximates osculating circle of a point on the vertical profile curve. Further, measurement model has been devised by a novel procedure estimating curvatures from circles approximating trajectory sensed by vertical acceleration and pitch-rate curvature. Proposed method has been field-tested by comparing its output with LTRC’s recorded data.

[1]  Mikihito Kobayashi,et al.  Condition monitoring of shinkansen tracks using commercial trains , 2008 .

[2]  Norden E. Huang,et al.  Fast Inspection and Identification Techniques for Track Irregularities Based on HHT Analysis , 2012, Adv. Data Sci. Adapt. Anal..

[3]  Alan V. Oppenheim,et al.  Discrete-time signal processing (2nd ed.) , 1999 .

[4]  Rolf Dollevoet,et al.  Improvements in Axle Box Acceleration Measurements for the Detection of Light Squats in Railway Infrastructure , 2015, IEEE Transactions on Industrial Electronics.

[5]  Victoria J. Hodge,et al.  Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey , 2015, IEEE Transactions on Intelligent Transportation Systems.

[6]  Peter Händel,et al.  Onboard Estimation and Classification of a Railroad Curvature , 2010, IEEE Transactions on Instrumentation and Measurement.

[7]  Andrea Collina,et al.  A measurement system for quick rail inspection and effective track maintenance strategy , 2007 .

[8]  Piotr Kozierski,et al.  Resampling in particle filtering : comparison , 2013 .

[9]  Paul Weston,et al.  The utility of continual monitoring of track geometry from an in-service vehicle , 2014 .

[10]  C. Roberts,et al.  Monitoring vertical track irregularity from in-service railway vehicles , 2007 .

[11]  A. Pressley Elementary Differential Geometry , 2000 .

[12]  Sunghoon Choi,et al.  A Mixed Filtering Approach for Track Condition Monitoring Using Accelerometers on the Axle Box and Bogie , 2012, IEEE Transactions on Instrumentation and Measurement.

[13]  Hitoshi Tsunashima,et al.  Track geometry estimation from car-body vibration , 2014 .

[14]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[15]  Simo Srkk,et al.  Bayesian Filtering and Smoothing , 2013 .

[16]  Stefano Bruni,et al.  Estimation of long wavelength track irregularities from on board measurement , 2008 .

[17]  Stuart L. Grassie,et al.  Measurement of railhead longitudinal profiles: a comparison of different techniques , 1996 .

[18]  James H. Garrett,et al.  Track monitoring from the dynamic response of a passing train: A sparse approach , 2017 .

[19]  Bhavana Bhardwaj,et al.  Railroad Track Condition Monitoring Using Inertial Sensors and Digital Signal Processing: A Review , 2019, IEEE Sensors Journal.

[20]  Alfredo Cigada,et al.  Rail inspection in track maintenance: A benchmark between the wavelet approach and the more conventional Fourier analysis , 2007 .

[21]  Stefano Bruni,et al.  Estimation of lateral and cross alignment in a railway track based on vehicle dynamics measurements , 2019, Mechanical Systems and Signal Processing.

[22]  P.J. Wolfs,et al.  An autonomous, low cost, distributed method for observing vehicle track interactions , 2006, Proceedings of the 2006 IEEE/ASME Joint Rail Conference.

[23]  Xingjian Jing,et al.  A comprehensive review on vibration energy harvesting: Modelling and realization , 2017 .

[24]  Patrick Robertson,et al.  Measurement and analysis of train motion and railway track characteristics with inertial sensors , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[25]  Roger M. Goodall,et al.  Strategies and techniques for safety and performance monitoring on railways , 2009 .

[26]  Roger M. Goodall,et al.  Monitoring lateral track irregularity from in-service railway vehicles , 2007 .

[27]  Maksym Spiryagin,et al.  Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review , 2019, IEEE Sensors Journal.

[28]  James H. Garrett,et al.  Track-monitoring from the dynamic response of an operational train , 2017 .

[29]  Paul Weston,et al.  Perspectives on railway track geometry condition monitoring from in-service railway vehicles , 2015 .

[30]  Laura Montalbán,et al.  Determination of Rail Vertical Profile through Inertial Methods , 2011 .

[31]  Simo Särkkä,et al.  Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.

[32]  Roger M. Goodall,et al.  Condition Monitoring Opportunities Using Vehicle-Based Sensors , 2011 .

[33]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[34]  Daniel Cantero,et al.  Determination of railway track longitudinal profile using measured inertial response of an in-service railway vehicle , 2018 .