Pierluca D'Adamio

Development of a New Time Domain-Based Algorithm for Train Detection and Axle Counting

This paper presents an innovative train detection algorithm, able to perform the train localisation and, at the same time, to estimate its speed, the crossing times on a fixed point of the track and the axle number. The proposed solution uses the same approach to evaluate all these quantities, starting from the knowledge of generic track inputs directly measured on the track (for example, the vertical forces on the sleepers, the rail deformation and the rail stress). More particularly, all the inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. This approach has the advantage to be simple and less invasive than the standard ones (it requires less equipment) and represents a more reliable and robust solution against numerical noise because it exploits the whole shape of the input signal and not only the peak values. A suitable and accurate multibody model of railway vehicle and flexible track has also been developed by the authors to test the algorithm when experimental data are not available and in general, under any operating conditions (fundamental to verify the algorithm accuracy and robustness). The railway vehicle chosen as benchmark is the Manchester Wagon, modelled in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed to verify the performance and the robustness of the proposed algorithm, and the results are quite promising. The research has been carried out in cooperation with Ansaldo STS and ECM Spa.

A localization algorithm for railway vehicles

Odometry is a safety on-board subsystem of modern railway Automatic Train Protection (ATP) and Automatic Train Control (ATC) and his main task is the estimation of instantaneous speed and the travelled distance of the railway vehicle. An accurate estimation is mandatory, because an error (residual) on the train position may lead to a dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, the proposed algorithm exploits data fusion of different inputs coming from a redundant sensor layout: in particular, the proposed strategy consists of a sensor fusion between the information coming from a tachometer and an IMU (Inertial Measurements Unit) is carried out. A 3D multibody model has been designed so at to simulate the sensor outputs. Within the framework of the presented research, a custom IMU, designed by ECM S.p.a. has been built. The IMU board is then tested via a dedicated HIL test rig (Hardware in the Loop) that includes an industrial robot able to reproduce the motion of the railway vehicle. The performances of the innovative localization algorithm have been evaluated by generating the experimental outputs. The main aim of this work is the development of an innovative localization algorithm for railway vehicles able to enhance the speed and position estimation accuracy of the classical odometry algorithms, such as the Italian SCMT (Sistema Controllo Marcia Treno). The results highlight a good improvement of the position and speed estimation performances compared to those obtained with classical SCMT algorithms, currently in use on the Italian railway network.

A New Strategy for Dynamic Weighing in Motion of Railway Vehicles

Weighing-in-motion (WIM) systems automatically perform the dynamic weighing of railway vehicles while the trains are running on the lines through a reasonable number of measurement points placed along the track. Such intelligent systems may overcome disadvantages in terms of costs and traffic management, which are typical of conventional static weighing systems. In this paper, we present an innovative algorithm for dynamical WIM applications aimed at estimating the axle and wheel loads of a generic train composition by means of indirect track measurements. The new approach allows the axle loads estimation at high vehicle speeds and can be customized for several input track measurements (rail shear, rail bending, vertical forces on the sleepers, etc.) as well as a combination of them. Consequently, it can be employed in different kinds of measurement stations. Having once studied the accuracy of the algorithm in estimating the loads, the same novel procedure is used to estimate the center-of-gravity position of the railway vehicle to avoid dangerous imbalances. The algorithm can receive as inputs both experimental and simulated data; simulated data are fundamental to test the algorithm (in terms of accuracy and robustness) under any operating conditions when experimental data are not available. A wide simulation campaign has been carried out to test the algorithm performances, obtaining promising results. In the near future, the proposed approach will be validated through suitable data coming from experimental tests organized in collaboration with Ansaldo STS and ECM SpA, which are the industrial partners of this research project.

Next generation of smart sensorless drives for sustainable underwater vehicles

Both Autonomous and Remotely Operated Underwater Vehicle play a key role in the development of offshore industrial installations assuring both cost and safety improvements in a very harsh environment. In order to improve performances, reliability and sustainability of underwater robotics, there is the need of a new generation of motors and drives devoted to this kind of applications. In this work authors focus their attention to most interesting aspect of the development of this new generation of products, introducing design examples and results of experimental activities performed on motors installed on Tifone, Marta and Feel Hippo AUVs.

An innovative algorithm for train detection

Train detection is a very important research issue affecting vehicles and line safety. Currently, the European Train Control System ETCS (a signalling, control and train protection system) Level 1 and 2 provide the train localization functionalities by using track circuits and/or axle counter systems: the problem of these solutions is represented by the high cost of track circuit and axle counter installation and of the related equipment management. This paper presents an innovative train detection algorithm, able to perform the train localization, by estimating its speed, crossing time instants and axle number. The aim of the proposed solution is to use the same processing approach to evaluate all these quantities, starting from the knowledge of the vertical loads on the sleepers directly measured on the track. The inputs are processed through cross-correlation operations to extract the required information in terms of speed, crossing time instants and axle counter. A suitable model of railway vehicle and track has been also developed to test the algorithm when experimental data are not available. The railway vehicle chosen as benchmark is the Manchester Wagon, implemented in the Adams VI-Rail environment. The physical model of the flexible track has been implemented in the Matlab and Comsol Multiphysics environments. A simulation campaign has been performed in order to verify the performance of the proposed algorithm, under different operative conditions. The research has been carried out in cooperation with Ansaldo STS and ECM.

An innovative high speed Weigh in Motion system for railway vehicles

The accurate estimation of the axle loads and the correct detection of overloads and imbalances, represent a primary concern for railways management companies, since they are strictly related to traffic safety and maintenance planning of the track. Weigh in Motion (WIM) systems aim at the dynamic weighing of railway vehicles through a reasonable number of measurement stations placed along the track. Such systems may overcome disadvantages in terms of costs and traffic management exhibited by conventional static weighing systems. In this paper the authors present an innovative algorithm for high speed WIM applications to estimate the wheel loads of trains by means of indirect track measurements. The formulation of the algorithm is quite general and it can be customized for several track measurements; consequently it can be employed in different typologies of measurement stations. The WIM algorithm processes the set of experimental physical quantities chosen as track inputs by means of estimation procedures based on least square (LSQ) minimization techniques. The vertical loads on the train wheels are computed from the measurements according to the assumption that the effects of the single wheel loads on the track are approximately superimposable. The whole WIM architecture has been developed in cooperation with Ansaldo STS and ECM SpA.

An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.

Weigh in Motion systems for railway vehicles: Performance and robustness analysis

One of the most important issue in the railway research is represented by the accurate estimation of the axle loads of railway vehicles. Weigh in Motion (WIM) devices are designed to measure loads with the vehicle in motion, making the weighing process more efficient. This paper is focused on an innovative algorithm for high speed WIM applications able to estimate the wheel loads of trains by means of indirect track measurements. The novelty of the proposed estimation method are its generality, the possibility to be used with different layouts and its robustness against numerical and measure noise. The main estimation procedure is based on least square (LSQ) minimization techniques, used to process the set of experimental physical input. The whole WIM architecture has been developed in cooperation with Ansaldo STS and ECM SpA.

Development of sensorless PM servo-system for harsh environments

Aim of this work is the development of efficient and robust filters for speed and position controls to be operated in harsh environments and in particular for underwater applications. PM motors are the most commonly adopted solutions for this kind of applications where the reliability of the component has important consequences both for the safety of the vehicle and the sustainability of the application. Common applications are the inspections of valuable underwater sites or the maintenance of potentially dangerous industrial facilities such as submerged plants of Oil&Gas industry. In particular in this work authors have concentrated their attention on the part concerning robust and efficient speed and position estimators for sensorless drive systems.