A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Escalona, José L.; Urda, Pedro; Muñoz, Sergio

doi:10.3390/s21030683

Cited by 19 publications

(22 citation statements)

References 22 publications

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“…Lateral accelerations have been analyzed to study gauge and lateral alignment deviations and compared with measurements provided by a track monitoring trolley. The paper [34] describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder.…”

Section: Review Of the Current Studiesmentioning

confidence: 99%

Monitoring and Evaluation of the Lateral Stability of CWR Track

Atapin

Bondarenko

Sysyn

et al. 2021

J Fail. Anal. and Preven.

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Continuous welded rail (CWR) track has great advantages of low-cost maintenance, environmental influence, and ride comfort. However, the CWR track is subjected to the longitudinal stresses in rails due to temperature influence as well the inhomogeneous stress accumulation due to train loadings. The stresses cause the accelerated track lateral irregularity accumulation that without timely maintenance can cause track buckling. The present paper present a method of the CWR track lateral stability estimation during its lifecycle using the track geometry monitoring information from the track measurement cars. The methods proposes a systematic approach of track stability evaluation based on multiple criteria of track stability evaluation. It takes into account the lateral resistance of the track, actual temperatures, and the lateral geometry condition of the track. The presented case study of a half-year track geometry monitoring and the track stability evaluation in a track curve shows the practical possibility of the recent detection of the track sections with low lateral stability and buckling prevention.

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Section: Review Of the Current Studiesmentioning

confidence: 99%

Monitoring and Evaluation of the Lateral Stability of CWR Track

Atapin

Bondarenko

Sysyn

et al. 2021

J Fail. Anal. and Preven.

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“…On one hand, validation of a multibody model thanks to specific sensors is widespread and ranges from many applications with various sensors, for example, a railway track geometry measurement based on IMUs, cameras, and encoders [ 13 ]; model estimation of a vehicle suspension system with strain gauges [ 14 ]; gait analysis through IMUs compared to optical motion capture [ 15 ]; validation of a axial piston pump model via force and pressure sensors [ 16 ]; and acceleration and force piezoelectric sensors to test a wind turbine flexible multibody model [ 17 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Haptic Devices Based on Real-Time Dynamic Models of Multibody Systems

Docquier

Timmermans

Paul

2021

Sensors

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Multibody modeling of mechanical systems can be applied to various applications. Human-in-the-loop interfaces represent a growing research field, for which increasingly more devices include a dynamic multibody model to emulate the system physics in real-time. In this scope, reliable and highly dynamic sensors, to both validate those models and to measure in real-time the physical system behavior, have become crucial. In this paper, a multibody modeling approach in relative coordinates is proposed, based on symbolic equations of the physical system. The model is running in a ROS environment, which interacts with sensors and actuators. Two real-time applications with haptic feedback are presented: a piano key and a car simulator. In the present work, several sensors are used to characterize and validate the multibody model, but also to measure the system kinematics and dynamics within the human-in-the-loop process, and to ultimately validate the haptic device behavior. Experimental results for both developed devices confirm the interest of an embedded multibody model to enhance the haptic feedback performances. Besides, model parameters variations during the experiments illustrate the infinite possibilities that such model-based configurable haptic devices can offer.

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“…The key difference between the usage of first principals presented here follows. Classical methods impose the form of the estimation and control (typically negative feedback with gains) and they have very recently been applied to railway vehicles [ 21 ], biomechanical applications [ 22 ], and remotely operated undersea vehicles [ 23 ], electrical vehicles [ 24 ], and even residential heating energy consumption [ 25 ] and multiple access channel usage by wireless sensor networks [ 26 ]. Deterministic artificial intelligence uses first principals and optimization for all quantities but asserts a desired trajectory.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

Sands

2021

Sensors

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To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is formulated using Pontryagin’s treatment of Hamiltonian systems resulting in optimal and near-optimal results dependent upon the algorithm option chosen. Feedback is proposed to re-initialize the initial values of a reformulated two-point boundary value problem rather than using state feedback to form errors that are corrected by tuned estimators. Four algorithm options are proposed with two optional branches, and all of these are compared to three manifestations of classical estimation methods including linear-quadratic optimal. Over ten-thousand simulations were run to evaluate each proposed method’s vulnerability to variations in plant parameters amidst typically noisy state and rate sensors. The proposed methods achieved 69–72% improved state estimation, 29–33% improved rate improvement, while simultaneously achieving mathematically minimal costs of utilization in guidance, navigation, and control decision criteria. The next stage of research is indicated throughout the manuscript: investigation of the proposed methods’ efficacy amidst unknown wave disturbances.

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A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Cited by 19 publications

References 22 publications

Monitoring and Evaluation of the Lateral Stability of CWR Track

Monitoring and Evaluation of the Lateral Stability of CWR Track

Haptic Devices Based on Real-Time Dynamic Models of Multibody Systems

Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

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