Experimental investigation on the use of multiple very low-cost inertial-based devices for comfort assessment and rail track monitoring

The periodic rail track inspection is mandatory to ensure ride comfort and operational safety. However, conventional monitoring technologies have high costs, stimulating research on low-cost alternatives. In this regard, this paper presents the first experimental results on the use of multiple very low-cost sensors aboard trains for vibration monitoring, proposing a collective approach to provide more accurate and robust results. Nine devices comprising commercial-grade inertial sensors were tested in different distributions aboard a high-speed track recording train. Frequency weighted accelerations were calculated in accordance with ISO 2631 standard as comfort and indirect track quality index. As expected, vertical and lateral results were correlated with, respectively, track longitudinal level (range D1, maximum correlation coefficient of 0.86) and alignment (range D2, maximum correlation coefficient of 0.60), with numerically similar results when considering the fused signal. The collective approach's potential was proven as a result of the noise reduction and the discrepant sensor identification

Improving the safety and efficiency of rail yard operations using robotics

textSignificant efforts have been expended by the railroad industry to make operations safer and more efficient through the intelligent use of sensor data. This work proposes to take the technology one step further to use this data for the control of physical systems designed to automate hazardous railroad operations, particularly those that require humans to interact with moving trains. To accomplish this, application specific requirements must be established to design self-contained machine vision and robotic solutions to eliminate the risks associated with existing manual operations. Present-day rail yard operations have been identified as good candidates to begin development. Manual uncoupling, in particular, of rolling stock in classification yards has been investigated. To automate this process, an intelligent robotic system must be able to detect, track, approach, contact, and manipulate constrained objects on equipment in motion. This work presents multiple prototypes capable of autonomously uncoupling full-scale freight cars using feedback from its surrounding environment. Geometric image processing algorithms and machine learning techniques were implemented to accurately identify cylindrical objects in point clouds generated in real-vi time. Unique methods fusing velocity and vision data were developed to synchronize a pair of moving rigid bodies in real-time. Multiple custom end-effectors with in-built compliance and fault tolerance were designed, fabricated, and tested for grasping and manipulating cylindrical objects. Finally, an event-driven robotic control application was developed to safely and reliably uncouple freight cars using data from 3D cameras, velocity sensors, force/torque transducers, and intelligent end-effector tooling. Experimental results in a lab setting confirm that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock up to two miles per hour. Additionally, subcomponents of the autonomous pin-pulling system (APPS) were designed to be modular to the point where they could be used to automate other hazardous, labor-intensive tasks found in U.S. classification yards. Overall, this work supports the deployment of autonomous robotic systems in semi-unstructured yard environments to increase the safety and efficiency of rail operations.Mechanical Engineerin

A railway track reconstruction method using robotic vision on a mobile manipulator: a proposed strategy

Autonomous robot integration in railways infrastructure maintenance accelerates the digitization and intelligence of infrastructure survey & maintenance, providing high-efficiency and low-cost execution. This paper proposes a health assessment based on 3D reconstruction technology for railway track maintenance using a mobile robotic sensing platform. By combining multiple sensing and taking advantage of a robotic manipulator, a digital model of the target track components is built by a robot-actuated vision system which provides better 3D structural and surface condition reconstruction. Global geo-location and surrounding laser scanning are integrated to reinforce the digital completeness of the model for intelligent management. The new method consists of the following steps: First, according to scheduled maintenance tasks, a Robotics Inspection and Repair System (RIRS) navigates to the task location and uses the onboard depth camera for positioning. Then robot-mounted vision system is guided with an automated trajectory to build the 3D reconstruction of the track or repair object using the vision modeling technique. Finally, the 3D reconstructed model is fused with surrounding mapping of depth vision and Lidar scanning. Both laboratory tests and a realistic track test validated the feasibility of the proposed method by creating an accurate 3D reconstructed model. The modeled rail steel section size is quantitively compared with the ground truth in dimension, demonstrating good accuracy with a size error of less than 0.3 cm. The main contribution includes: (1) unmanned automatic 3D reconstruction by a robotic mobile manipulator, (2) the technique trims the reconstruction details & data to the specific maintenance goal or components, which supports the infrastructure maintenance towards the high-detailed & target-oriented digital management. This combination strategy of robotic automation and sensor fusion lies down a promising foundation for automated digital twin establishment for railway maintenance with autonomous RIRS, and upgrades technology readiness and digital intelligence for maintenance managementEuropean Union funding: 881574/82625

Combining Sensors and Multibody Models for Applications in Vehicles, Machines, Robots and Humans

The combination of physical sensors and computational models to provide additional information about system states, inputs and/or parameters, in what is known as virtual sensing, is becoming increasingly popular in many sectors, such as the automotive, aeronautics, aerospatial, railway, machinery, robotics and human biomechanics sectors. While, in many cases, control-oriented models, which are generally simple, are the best choice, multibody models, which can be much more detailed, may be better suited to some applications, such as during the design stage of a new product

X2Rail-4 D7.2 - OTI Technology Migration

The aim of the work was to look at the introduction of On-board train integrity (OTI) from different perspectives. Different OTI product classes are considered in their areas of application as well as the various railway market segments, with particular attention being paid to the freight transport sector, as this is particularly complex in terms of optimised wagon equipment due to single wagon traffic. To accompany the development of the on-board train integrity solutions up to TRL7, an outlook on the technology migration of the OTI is given. The work aimed at is to identify optimised migration paths for the rollout of OTI technology. To achieve this, boundary conditions are analysed in terms of surrounding migration strategies in the control and signalling of railway transport as well as migration conditions for the different market segments. Based on the technology specifications from the X2Rail-2 and X2Rail-4 projects, representative scenarios have been defined to apply the migration strategy. An optimization methodology was developed and computationally modelled and then applied to a railway network with an operating program. Based on the results of the optimization model, an economic evaluation of the different OTI migration strategies was performed. Life cycle cost analysis has been done to compare monetary effects of the different migration paths as well as the effects for different stakeholder

Manufacturing and characterisation of a fibre optic acoustic emission sensor

The value of Remote Condition Monitoring for the real-time evaluation of the structural integrity of critical components is undeniable. Fibre-reinforced polymer composites are a class of materials which offer significant advantages over conventional metal alloys used for manufacturing load bearing structures in cases where weight and/or energy consumption need to be kept to a minimum, for example automotive and aerospace applications. This is due to the excellent strength to weight ratio that FRPCs exhibit. However, their strongly anisotropic microstructure of poses significant challenges for Non-Destructive Evaluation of the actual structural health of components made from such materials. Acoustic Emission is a passive condition monitoring technique based on the detection of elastic stress waves emitted when damage evolves in a structure. Conventional piezoelectric AE sensors need to be surface-mounted as their embedding in FRPCs is impractical. Fibre Optic Acoustic Emission Sensors (FOAES) offer a distinct advantage since they are light weight, have small size and can be effectively embedded in composite laminates. Moreover, they can be multiplexed with the entire structure being monitored more effectively. This study has focused in the evaluation of the manufacturing process and characterisation of FOAES. Comparison of their performance with conventional commercial sensors was carried out

Active thermography for the investigation of corrosion in steel surfaces

The present work aims at developing an experimental methodology for the analysis of corrosion phenomena of steel surfaces by means of Active Thermography (AT), in reflexion configuration (RC). The peculiarity of this AT approach consists in exciting by means of a laser source the sound surface of the specimens and acquiring the thermal signal on the same surface, instead of the corroded one: the thermal signal is then composed by the reflection of the thermal wave reflected by the corroded surface. This procedure aims at investigating internal corroded surfaces like in vessels, piping, carters etc. Thermal tests were performed in Step Heating and Lock-In conditions, by varying excitation parameters (power, time, number of pulse, ….) to improve the experimental set up. Surface thermal profiles were acquired by an IR thermocamera and means of salt spray testing; at set time intervals the specimens were investigated by means of AT. Each duration corresponded to a surface damage entity and to a variation in the thermal response. Thermal responses of corroded specimens were related to the corresponding corrosion level, referring to a reference specimen without corrosion. The entity of corrosion was also verified by a metallographic optical microscope to measure the thickness variation of the specimens