Project VIMTO: A new system for the vibration and impact monitoring of tram operations

Disturbance to building occupants caused by tram-generated ground-borne noise and vibration presents a significant barrier to the expansion of tram networks in our cities. Furthermore, such disturbance is often an indicator of deteriorating track infrastructure. Monitoring and understanding ground-borne noise and vibration is therefore a key priority for tram operators. Project VIMTO (Vibration and Impact Monitoring of Tram Operations) is concerned with developing a new system for monitoring vibration and impact of trams, whereby the vehicles themselves are used as the primary monitoring instrument. Low-cost vehicle-mounted instrumentation is being used to record axle-box vibration signatures, along with positioning data, to ‘map’ a tram network in terms of its propensity to generate vibration. Such mapping aims to offer near real-time continuous monitoring, enabling the formulation of more efficient, optimised maintenance strategies. This paper describes the current development system and presents some initial results from trials on the Midland Metro, UK.This work was funded by the EPSRC Impact Acceleration Account (Grant No. EP/K503757/1) and by the Cambridge Centre for Smart Infrastructure and Construction (EP/I019308/1)

A handheld diagnostic system for 6LoWPAN networks

The successful deployment of low-power wireless sensor networks (WSNs) in real application environments is a much broader exercise than just the simple instrumentation of the intended monitoring site. Many problems, from node malfunctions to connectivity issues, may arise during commissioning of these networks. These need to be corrected on the spot, often within limited time, to avoid undesired delays in commissioning and yet a fully functional system does not guarantee that no new problems will occur after leaving the site. In this paper we present the first ever (to our knowledge) implementation of a handheld diagnostic system for fast on-site commissioning of low-power IPv6 (6LoWPAN) WSNs as well as troubleshooting of network problems during and after deployment. This system can be used where traditional solutions are insufficient to ascertain the root causes of any problems encountered at no additional complexity in the implementation of the WSN. The embedded diagnosis capability in our system is based on a lightweight decision tree that distills the functioning of communication protocols in use by the network, with a major focus on interoperable IPv6 standards and protocols for low-power WSNs. To show the applicability of our system, we present a set of experiments based on results from a real deployment in a large construction site. Through these experiments, important performance insights are gained that can be used as guidelines for improvement of operation and maintenance of 6LoWPAN networks.This research has been funded by the EPSRC Innovation and Knowledge Centre for Smart Infrastructure and Construction project (EP/K000314/1). The authors wish to thank Costain-Skanska Joint Venture (CSJV) and our industrial partner Crossrail for allowing access and instrumentation of the Paddington site referenced in this paper

Power-efficient piezoelectric fatigue measurement using long-range wireless sensor networks

Abstract In this paper we describe the design of a proof-of-concept wireless embedded sensor system for continuous strain cycle monitoring as a method for fatigue life assessment on civil structures. Monitoring of strain cycles is energy demanding, and therefore not suited to energy-constrained devices, as it requires continuous acquisition of strain data with a high sampling rate, followed by data processing using algorithms for peak-trough detection and cycle counting. To overcome this drawback, at the core of our proposed design is a piezoelectric-based analogue sensor system that can achieve as much as a factor of 9 increase in energy efficiency compared with the conventional approach. The key component is an analogue peak-trough detector that offloads the computation in peak-trough detection from the microcontroller, thus eliminating the need for continuous sampling. The function of the detector is coupled with an energy-efficient interrupt-driven software design for acquisition and strain cycles calculation, which is carried out by using a standard form of the rainflow cycle counting algorithm. For wireless communication and networking, LoRa and LoRaWAN are adopted as core modules. We illustrate the performance of our proposed solution by way of simulation and laboratory experiments. Results show a good agreement in measurement of strain cycles between our proposed system and the conventional approach. Thus, our solution proves to be promising for real fatigue measurement applications.</jats:p

Monitoring on the performance of temporary props using wireless strain sensing

Although temporary props have been extensively used in underground support systems, their actual performance is poorly understood, resulting in potentially conservative and over-engineered design. This paper presents the performance monitoring of 4 temporary props in an urban construction site using a newly developed wireless strain sensor node featuring a 24-bit ADC. For each prop, 6 strain gauges and 3 temperature sensors were directly attached onto the prop surface using super glue, and then connected to a wireless strain sensor node mounted in the middle span. Each sensor node transmitted both monitoring data and network diagnostic messages in near-real-Time over an IPv6-based (6LoWPAN) wireless mesh sensor network. The data were also stored locally at each node on a micro SD card. Extensive testing and calibration was undertaken in the laboratory to ensure that the system functioned as expected. The prop loads are presented without correction for temperature effects and compared with the design loads. The monitoring data reveal the development of loads in temporary props during excavation, the formation of the basement and the extraction of the props. The network performance characteristics in terms of message reception ratio and network topology evolution are also highlighted and discussed

Analysis of fiber-optic strain-monitoring data from a prestressed concrete bridge

This paper presents data from fiber-optic strain monitoring of the Nine Wells Bridge, which is a three-span, pretensioned, prestressed concrete beam-and-slab bridge located in Cambridgeshire in the United Kingdom. The original deployment at the site and the challenges associated with collecting distributed strain data using the Brillouin optical time domain reflectometry (BOTDR) technique are described. In particular, construction and deployment issues of fiber robustness and temperature effects are highlighted. The challenges of interpreting the collected data as well as the potential value of information that may be obtained are discussed. Challenges involved with relating measurements to the expected levels of prestress, including the effects due to debonding, creep, and shrinkage, are discussed and analyzed. This paper provides an opportunity to study whether two commonly used models for creep and shrinkage, adequately model data collected in field conditions.This work was supported by the following EPSRC grants: EP/D076870/1, Smart Infrastructure: Wireless Sensor Network System for Condition Assessment and Monitoring of Infrastructure; EP/I019308/1, Innovation Knowledge Centre for Smart Infrastructure and Construction; and EP/K000314/1, Innovation and Knowledge Centre for Smart Infrastructure and Construction - Collaborative Programme Tranche 1

Bridge Monitoring: A Practical Guide

Bridges are important infrastructure assets and provide vital road, rail and pedestrian lifelines for the communities they serve. Bridges permit transport across both natural boundaries such as rivers and valleys, and man-made barriers such as roads and rail lines. Bridges also represent points of interdependency between different transport networks, where a single failure can have far reaching social and economic consequences that extend well beyond the bridge itself. The resilience of these transport networks is dependent on the performance of the bridge assets. This book is intended to provide guidance on the monitoring of bridges, with a particular focus on the use of sensor technologies and bridge monitoring systems. It is aimed at a wide audience that includes bridge owners and operators, bridge engineering designers and consultants, civil engineering contractors, monitoring contractors and researchers. This guide presents a structured approach to the use of bridge monitoring systems, covering all stages from inception to decommissioning. The available technologies for bridge monitoring are many and varied with new technologies emerging all the time. This publication does not attempt to cover all possible technologies that may be used in bridge monitoring systems, nor does it seek to recommend any particular technologies as best practice. Nevertheless, the guide does describe some of the sensors and monitoring technologies commonly used on bridges. Many technologies used in bridge monitoring systems are mature and well understood, whilst others are emerging as potentially useful tools for adoption in the future

On the derivation of rail roughness spectra from axle-box vibration: Development of a new technique

Railhead roughness on railways is a cause of noise and vibration. Corrugation (a periodic form of roughness) can grow rapidly and unpredictably, generating high levels of noise and vibration. An emerging technique for monitoring rail roughness is by use of axle-box accelerometers on in-service trains, which can be more cost-effective than conventional inspection methods. Axle-box accelerometers measure the vibration induced by roughness, rather than the roughness itself, and hence require signal processing techniques to translate this vibration into suitable metrics of the railhead condition, such as a wavelength spectrum of roughness. This paper presents progress towards a new stochastic frequency-domain inverse method that derives wavelength-spectra of rail roughness from axle-box acceleration measurements. This method compensates for the effects of vehicle speed and track dynamic behaviour on axle-box acceleration, which have adversely affected previous methods that, for example, rely on calibration on a reference section of track or simply take the RMS of the axle-box acceleration. The practical implications of processing and presenting measurements in the frequency domain are discussed, including the effect of varying vehicle speed and the trade-off between resolution and statistical accuracy. An initial algorithm is proposed and demonstrated through timedomain simulations of a theoretical vehicle-track model. Accurate derivation of roughness from axle-box acceleration will facilitate future development of autonomous monitoring systems fitted to in-service trains that continuously 'map' the condition of a rail network in real time, enabling more efficient and proactive scheduling of rail maintenance

Data supporting 'Project VIMTO: A new system for the vibration and impact monitoring of tram operations'

Data necessary to re-create the figures in the paper: Project VIMTO: a new system for the vibration and impact monitoring of tram operations'.EPSRC grant nos. EP/K503757/1 and EP/I019308/

On the derivation of rail roughness spectra from axle-box vibration: Development of a new technique

Railhead roughness on railways is a cause of noise and vibration. Corrugation (a periodic form of roughness) can grow rapidly and unpredictably, generating high levels of noise and vibration. An emerging technique for monitoring rail roughness is by use of axle-box accelerometers on in-service trains, which can be more cost-effective than conventional inspection methods. Axle-box accelerometers measure the vibration induced by roughness, rather than the roughness itself, and hence require signal processing techniques to translate this vibration into suitable metrics of the railhead condition, such as a wavelength spectrum of roughness. This paper presents progress towards a new stochastic frequency-domain inverse method that derives wavelength-spectra of rail roughness from axle-box acceleration measurements. This method compensates for the effects of vehicle speed and track dynamic behaviour on axle-box acceleration, which have adversely affected previous methods that, for example, rely on calibration on a reference section of track or simply take the RMS of the axle-box acceleration. The practical implications of processing and presenting measurements in the frequency domain are discussed, including the effect of varying vehicle speed and the trade-off between resolution and statistical accuracy. An initial algorithm is proposed and demonstrated through timedomain simulations of a theoretical vehicle-track model. Accurate derivation of roughness from axle-box acceleration will facilitate future development of autonomous monitoring systems fitted to in-service trains that continuously 'map' the condition of a rail network in real time, enabling more efficient and proactive scheduling of rail maintenance