TOWARDS MACHINE VISION BASED RAILWAY ASSETS PREDICTIVE MAINTENANCE

The main goal of this paper is to present novel technologies that can contribute to safety, competitiveness, efficiency and operational reliability of Railway infrastructure through the development of innovative solutions for measuring and monitoring of railway assets based on machine vision. Measuring the transversal position of the wheels on the rail, as well as identification of the defects of the wheel and the rail (such as deformation of rail head edge, lateral wear, worn wheels, cracks in wheel and rail, rolling contact fatigue, corrugation and other irregularities) can increase reliability and lower maintenance costs. Currently, there is a need on the market for the innovative solution, namely the on-board high-speed stereo camera system augmented with a system that projects custom pattern (fringe scanner system) for measuring the transversal position of the wheels on the rail, robust to environmental conditions and waste along the track that can provide reliable measurements of transversal position of the wheels up to 200 km/h. New trends in Precise Industrial 3D Metrology are showing that stereo vision is an absolute must have in modern specialized optical precision measuring systems for the three-dimensional coordinate measurement

Optimising the environmental impact of deep foundations

A recent UN report has shown that the construction industry is one of the seven major sectors that contribute significantly to environmental pollution and was responsible for around 20% of energy-related CO2 emissions in 2020, and this is expected to increase during the upcoming years unless preventive actions are taken (UN Environment program, 2021). Many studies have addressed the carbon footprint of superstructures including life cycle assessments, trials to reduce the quantity of material used in construction and discovering new production techniques with lower environmental impact (Hawkins et al., 2020). However, the carbon footprint of substructures has only been investigated to a limited extent, this is believed to be due to a lack of certainty in the mechanical behaviour of soil and its interaction with structures as well as the construction complexity for deep foundations (Sandanayake et al., 2016)

Response-based methods to measure road surface irregularity: a state-of-the-art review

"jats:sec" "jats:title"Purpose"/jats:title" "jats:p"With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the state-of-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research."/jats:p" "/jats:sec" "jats:sec" "jats:title"Methods"/jats:title" "jats:p"Available articles regarding response-based methods to measure road surface condition were collected mainly from “Scopus” database and partially from “Google Scholar”. The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation."/jats:p" "/jats:sec" "jats:sec" "jats:title"Results"/jats:title" "jats:p"The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking."/jats:p" "/jats:sec Document type: Articl

Preview-based techniques for vehicle suspension control: a state-of-the-art review

Abstract Automotive suspension systems are key to ride comfort and handling performance enhancement. In the last decades semi-active and active suspension configurations have been the focus of intensive automotive engineering research, and have been implemented by the industry. The recent advances in road profile measurement and estimation systems make road-preview-based suspension control a viable solution for production vehicles. Despite the availability of a significant body of papers on the topic, the literature lacks a comprehensive and up-to-date survey on the variety of proposed techniques for suspension control with road preview, and the comparison of their effectiveness. To cover the gap, this literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview. The main formulations are reported for each control category, and the respective features are critically analysed, together with the most relevant performance indicators. The paper also discusses the effect of the road preview time on the resulting system performance, and identifies control development trends

On pre-emptive vehicle stability control

Future vehicle localisation technologies enable major enhancements of vehicle dynamics control. This study proposes a novel vehicle stability control paradigm, based on pre-emptive control that considers the curvature profile of the expected path ahead in the computation of the reference direct yaw moment and braking control action. The additional information allows pre-emptive trail braking control, which slows down the vehicle if the predicted speed profile based on the current torque demand is deemed incompatible with the reference trajectory ahead. Nonlinear model predictive control is used to implement the approach, in which also the steering angle and reference yaw rate provided to the internal model are varied along the prediction horizon, to account for the expected vehicle path. Two pre-emptive stability control configurations with different levels of complexity are proposed and compared with the passive vehicle, and two state-of-the-art nonlinear model predictive stability controllers, one with and one without non-pre-emptive trail braking control. The performance is assessed along obstacle avoidance tests, simulated with a high-fidelity model of an electric vehicle with in-wheel motors. Results show that the pre-emptive controllers achieve higher maximum entry speeds – up to ∼34% and ∼60% in high and low tyre-road friction conditions – than the formulations without preview.This work was supported in part by the Horizon 2020 Framework Programme of the European Commission under grant agreements no. 769944 (STEVE project) and no. 824311 (ACHILES project)

2022 Vehicle Dynamics seminar

The seminar is held annually. The full title of this year\u27s seminar was "2021 Vehicle Dynamics seminar -- Connected and Electric"