Robotic non-contact rail inspection feasibility study

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D

IPARTIMENTO DI

I

NGEGNERIA DELL

’E

NERGIA DEI

S

ISTEMI

,

DEL

T

ERRITORIO E DELLE

C

OSTRUZIONI

RELAZIONE PER IL CONSEGUIMENTO DELLA LAUREA MAGISTRALE IN INGEGNERIA GESTIONALE

Robotic Non-Contact Rail Inspection Feasibility Study

RELATORI IL CANDIDATO

Prof. Ing. Gino Dini Valentina Giordano

Dipartimento di Ingegneria Civile e Industriale valentina.giordano01@gmail.com

Dr. Isidro Durazo Cardenas

Through-Life Engineering Services Centre, Cranfield

Prof. Andrew Starr

Through-Life Engineering Services Centre, Cranfield

Sessione di Laurea del 03/10/2018 Anno Accademico 2017/2018

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SOMMARIO

La sicurezza delle ferrovie è una delle sfide più importanti per Network Rail. Grazie al miglioramento delle pratiche di manutenzione, il numero totale di difetti ferroviari è diminuito negli ultimi anni. Tuttavia, i difetti del piede ferroviario rimangono ancora critici. Questo lavoro di tesi, svolto presso Cranfield University, mira a studiare la fattibilità di un nuovo sistema di ispezione ferroviario senza contatto; come parte di un progetto europeo di ricerca sull'ispezione robotica. L’efficacia del laser doppler vibrometer e della camera termografica è stata valutata sperimentalmente. Inizialmente, in collaborazione con Network Rail, è stato determinato un range di difetti critici. Gli esperimenti sono stati condotti prima su piastre di metallo, per comprendere i principi della tecnologia e effettuare una pre-valutazione dei sistemi. Delle barre, rappresentative dei binari, sono state successivamente testate per completare la valutazione delle due tecnologie. In conclusione, entrambi hanno mostrato una buona potenzialità nel rilevare difetti critici. Tuttavia, sono necessari ulteriori test e analisi per massimizzarne i benefici.

ABSTRACT

Safe operation of railways is one of the most important challenges of Network Rail. Thanks to the improvement of maintenance practices, the total number of defects that occur on rails has decreased in recent years. However, damage occurring on the rail foot remains critical. This research, performed at Cranfield University, aims to study the feasibility of a new non-contact rail inspection system; as part of a wider European robotic inspection research project. Laser doppler vibrometer and thermography camera were experimentally evaluated, and their effectiveness assessed. Firstly, a range of critical defects was determined in collaboration with Network Rail. Experiments were performed on metal plates first, in order to understand the technology principles and carry out a pre-evaluation of the systems. I-beams simulating rails were tested subsequently to completely evaluate the systems’ effectiveness. In conclusion, LDV and IR camera have shown good potential to detect critical rail defects and be used in a robotic inspection system. However, further tests and analysis are needed to maximise the benefits of these technologies.

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ACKNOWLEDGEMENTS

The author wishes to thank all the persons who contributed to the project by providing knowledge and support.

First, I am grateful to Professor Gino Dini for having given me the opportunity to make this amazing experience thanks to the double degree partnership with Cranfield University. My sincere gratitude to Dr Isidro Durazo Cardenas for his encouragement and constant supervision as well as for providing necessary information to carry out the project.

Many thanks to Dr Pavan Addepalli for his constructive suggestions and assistance during the analysis of results. Thanks to Luke Oakey, for his technical support during the lab experimentations.

In addition, I would like to express my gratitude to my sponsor Network Rail having given me the opportunity to undertake this project and to the associate supervisor Andrew Starr, for his useful knowledge shared.

Special acknowledgments to Arianna Montanaro, Federica Giannini and Francesca Ferrati, to Rodrigue Berniolles and all my friends for their essential support and enthusiasm, and for having made this experience unforgettable.

My grateful thanks are also extended to Carlotta Franchi, Giusy Lanzillo and Mariagiulia Garcea for being part to all the best moments of these 5 years.

Finally, my deepest gratitude to my family for having always believed in me, having encouraged and made all this possible.

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LIST OF ABBREVIATIONS

IR LDV NDT LRUT PEC Infrared thermography Laser Doppler Vibrometer Non-destructive testing Long Range Ultrasonic Testing Pulse eddy current

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1 Introduction

1.1 Background

Network Rail owns, operates and maintains Britain’s railway infrastructure. It includes 20,000 miles of track, 30,000 bridges, tunnels and viaducts and thousands of signals, level crossings and stations. Daily, 4.6 million journeys are made on the British railway. Network Rail work is to provide a safe and reliable railway and expand the nation’s network to deal with the continuous increase of passenger journeys over years. Working in partnership with the Government and industry, it is making £50 billion investment in innovation to improve railway safety and efficiency, such as the Shift2Rail project, (NetworkRail, 2017). The final aim of this project is to incorporate inspection requirements into an autonomous rail vehicle to undertake inspection and repair operations with an operator supervising remotely,(Catapult, 2018).

1.2 Problem definition

Safety of railway represents the biggest challenge that Network Rail has to face with. The overall cost of rail failure, excluding pre-emptive treatment such as rail grinding, and derailments, is between €750-1500 per km per year, (NetworkRail, 2017). Britain’s total track length is about 32000 kilometres so that, the total annual cost is about €24-48 million only in the UK. This is only a rough estimation but it provides a representation of the problem’s magnitude, (Cannon et al., 2003).

Several non-destructive testing (NDT) techniques are currently used to inspect rail tracks. Each of them has the ability to detect different kind of defects and, a combination of more techniques is usually applied to increase the probability of defect detection. However, there are types of defects, such as those on the rail foot, that are still difficult to detect by using the current systems. In fact, as shown in Figure 1, they have the highest impact on the total number of rail flaws. In this project, laser doppler vibrometer and thermography analysis will be evaluated for the purpose and, their effectiveness assessed in order to introduce them in a new robotic inspection system.

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Figure 1 Types of rail flaws in percentage (Whitney, 2018)

1.3 Aim and Objectives

Aim: study the feasibility of a new non-contact inspection system comprising laser doppler vibrometer and thermography.

Objectives:

• Conduct a critical literature review

• Conduct laboratory experiments to evaluate and characterise a range of defects on rail tracks

• Analyse results to evaluate the new system effectiveness • Review robot interface and provide recommendations

The thesis’ scope is to assess the systems effectiveness and their capability to detect rail foot defects. For this purpose, visual comparisons between results of the sound and defective specimens were performed. The scope does not include deep analysis of the material and its properties or any application of signal processing algorithms.

2 Literature Review

2.1 Network Rail

According to (Kumar et al., 2010), rail defects have increased in severity and occurrence over the last 20-30 years due to different reasons. Flaws that threaten the railway system safety can have different root causes, sizes, and locations. However, if any recovery action is undertaken, all of them may develop into rail breakages causing catastrophic events, (Mustapha and Matori, 2015). Security of railway represents the main challenge and the main

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priority for Network Rail. Over the last 5 years, train accident risk has been reduced by 38% thanks to better inspection techniques, asset management, and improved operations, (NetworkRail, 2018b). In order to increase the railway performances, Network Rail makes lots of investments in innovation, e.g. its participation in the Shift2Rail initiative.

2.1.1 Shift2Rail

Shift2Rail is the first European private-public partnership research initiative for the integration of advanced technologies into innovative solutions. In order to meet the changing transport needs, the aim of the initiative is to double the European rail system capacity and increase its reliability and punctuality by 50%. Developing new inspection systems and methods is essential to achieve this aim, (Borghini, 2017).

2.2 Type of Defects and Causes

According to (Papaelias, Roberts and Davis, 2008), rail defects can be classified into 3 different categories based on their location, Figure 2. They can be caused by different reasons such as manufacturing process, improper usage, handling or installation and structural degradation caused by fatigue or rail corrosion, (Mustapha and Matori, 2015).

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Figure 4 Types and location of rail flaws (Clark, 2004)

Figure 3 rail track (Clark, 2004)

Rail defects Rail head Internal defects Transverfe cracks Horizontal cracks Longitudinal-vertical cracks Surface defects RCF Head checks Gauge-corner cracks Squats Corrugation Rail web Longitudinal-vertical cracks

Bolt hole star cracks Rail foot Transverse fatigue cracks Longitudinal vertical cracks Manufacturing

process Improper usage

Structural degradation

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While flaws on rail head and web can be successfully detected by traditional NDT techniques, rail foot defects are still critical.

2.2.1 Rail Foot Defects

Two kinds of flaws have been classified from the literature as foot defects, the transverse fatigue cracks, Figure 5, and the longitudinal-vertical cracks, Figure 6, (Cannon, 2003).

2.2.1.1 Transverse Fatigue Crack

As result of high bending, torsional and residual stresses, very small underside foot cracks, (RAIB, 2014), can generate the total fracture of the track. This kind of defect is not easily visible and it is difficulty detected, in particular when it is located outside the projected web area, (Cannon, 2003). Most of NDT techniques currently used for inspection are unable to detect it. The literature shows that only ultrasonic systems such as ultrasonic transducers, laser ultrasonic and long range ultrasonic testing are sensitive to rail foot defects, (Mustapha and Matori, 2015).

Figure 5 Transverse fatigue crack on the rail foot (Cannon, 2003)

2.2.1.2 Longitudinal-vertical Cracks

This defect usually starts as longitudinal-vertical split which turns towards the edge of the rail (foot) resulting in a “half-moon” break out of material, Figure 6, Figure 7. Its dangerousness is due to the fact that the rail may fracture into several pieces, (Cannon, 2003). According to (NetworkRail, 2018) standard, the minimum longitudinal-vertical defect length detectable with a probability of 30% is 300 mm. On the other hand, the minimum transverse crack

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detectable by ultrasonic transducer is 5-10 mm deep but only if located beneath the web, (Whitney, 2018)

Figure 6 Longitudinal-vertical crack on rail foot (Cannon, 2003)

Figure 7 Half-moon damage on the rail foot (Cannon, 2003)

2.3 State-of-art of NDT Methods for Rail Track Inspection

In order to detect defects before they cause serious disasters, many NDT methods have been applied over decades. The most currently used were shown in Table 1.

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NDT technique Defects detected Performances

Ultrasonic Transducers Surface defects, head and web

internal defects, rail web and foot defects

Reliable manual inspections but foot defects can be missed. Less reliable in high-speed inspections.

Magnetic Induction Surface and near-surface head

defects

Adopted complementarily with ultrasonic transducers due to its limitations. It is not able to detect deep internal cracks and rail foot corrosions and, it is not reliable when inspection speed increases up to 35 km/h.

Pulsed Eddy Current (PEC) Surface and near-surface internal

defects

Good ability to detect small cracks, wheelburns, grinding marks and short-wave corrugations. Adversely affected by lift-off variations.

Visual Inspection Surface breaking defects, rail head

profile, corrugation, missing parts

Inspection velocity can reach 320 km/h. Smaller is the defect that has to be detected, slower has to be the inspection speed due to the higher image resolution needed.

Radiography Flaws in alumino-thermic welds Reliable for internal flaws in welds,

difficult to inspect by other techniques.It provides information regarding location, size, and nature of the internal defects. However, critical health and safety drawbacks characterised this system, it is time-consuming and has low performance in detecting transverse cracks.

Laser Ultrasonic Rail head, web and foot defects Reliable for internal defects.

Difficult to use it at high speeds.

Long Range Ultrasonic Testing (LRUT)

Surface defects, rail head, web and foot defects

Reliable in detecting transverse defects

Table 1 Summary of NDT techniques currently applied for rail inspections, (Papaelias, Roberts and Davis, 2008), (RAIB, 2014), (Mustapha and Matori, 2015).

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According to Table 1, only few NDT techniques are sensitive to rail foot defects thus, they will be deeply explained in the following sections.

2.3.1 Ultrasonic Transducers

Inspections using ultrasonic transducers are carried out either manually, using a portable equipment placed on push-trolleys, or by using high-speed vehicles carrying ultrasonic probes. A piezoelectric element generates a beam of ultrasonic energy that is transmitted into the rail and the scattered energy is read using a set of transducers. In order to maximise the probability to detect defects, the energy is transmitted at different incident angles (0°, 37°, 70°), Figure 8, (Papaelias, Roberts and Davis, 2008).

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In general, this equipment works quite well in detecting internal defects in rail head and web. Only pedestrian ultrasonic equipment is capable of detecting cracks in the central part of the rail foot, but its performance is not always reliable. Due to the long beam path, the speed at which testing can be carried out is restricted and when defects are located outside the projected web area, they are totally undetectable, Figure 9. Moreover, no cracks below 123 mm from the rail head are reported, that means cracks in the bottom of rail higher than 123 mm are probably undetectable as well, (RAIB, 2014).

Figure 9 Limited extent of crack detection in rail foot, (RAIB, 2014).

2.3.2 Laser Ultrasonic

Differently from conventional ultrasonic systems which use contact and non-contact transducer, laser ultrasonic has the ability to detect near-surface flaws as well as defects in the rail foot. This technology mixes the defect detection sensitivity of the ultrasonic systems with the flexibility of optical systems. It has the capability to perform rapid non-contact measurements, enabling inspections in hostile environments, and carry out reliable detection when the high temperature is not tolerated by other NDT systems, (Mustapha and Matori, 2015).

2.3.3 Range Ultrasonic Testing (LRUT)

Ultrasonic testing by using guided waves has the potentiality to detect the rail under-foot area over a range of 20 m. These waves can propagate in steel with low attenuations, however,

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there are factors that can drastically attenuate the signal reducing its propagation,(Gharaibeh, Mudge and Kappatos, 2011).

The main advantages of this technique are the ability to inspect a large area with only a single transducer location, the sensitivity to detect small flaws and the capability to inspect alumino-thermic welds, (Mustapha and Matori, 2015). However, this technology is still at early stages and only feasibility studies that demonstrate its application were found in the literature. Due to the severity of the rail foot defect occurrence and, disadvantages and limitations of the current NDT techniques, laser doppler vibrometer and thermography will be evaluated in this research.

2.4 Laser Doppler Vibrometer (LDV)

In order to perform measurements, the laser beam is pointed to the surface tested and the reflected beam is collected and analysed. Due to the doppler effect caused by the motion of the surface, the reflected beam has different frequency and, by exploiting that, the target surface can be assessed, (Castellini, Martarelli and Tomasini, 2006)

2.4.1 Basic principle

Laser is the acronym of Light Amplification by Stimulated Emission of Radiation which also explains its basic physical principle. Light is emitted by using an optical amplification process based on induced emission of photons. All the emitted photons have identical properties thus, the monochromatic and coherent light, which characterised lasers, is generated, (Haley and Pratt, 2017). Laser used for LDV is the helium-neon with a wavelength of 0.6328, (Lutzmann et al., 2018).

LDV principle is based on the detection of doppler shift from the reflected beam. The Doppler effect was observed for the first time by Christian Doppler, an austrian physics, who noticed that a change in wavelength and frequency occurs when observer and source are moving, (Prislan and Sven, 2008).

During LDV measurements, the light source is fixed, and the observed object is moving. This movement changes the frequency of the reflected beam and, by measuring this frequency, object velocity can be calculated. However, due to the high laser frequency (almost 4.74 x1014

Hz), a direct demodulation of the light is unfeasible. Thus, an interferometer is used to coherently mix the light scattered by the object and the reference beam. The intensity of the

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mixed beam, which has a frequency equal to the difference between the frequency of the reference and reflected beam, is measured by a photo-detector, (CPD, 2008).

Figure 10 Laser Doppler Vibrometer principle (Haley and Pratt, 2017)

In Figure 10, the laser beam, with frequency f0, is divided into reference and reflected beam

by the first beam splitter. The reference beam arrives directly to the photo detector by keeping the same frequency. While, the reflected beam is scattered by the vibrating target surface and its frequency changes due to the doppler shift. Therefore, the frequency measured, f’, will be:

𝑓′ _{= 𝑓} 0(

𝑐

𝑐 ± 𝑣) 2-1

Where c (299792458 m/s) is the propagation velocity of the wave and v is the target velocity. The ± sign depends on the object movement.

Considering that c>>v, the equation can be evolved in a Taylor series:

𝑓′ = 𝑓0 ( 𝑐 𝑐 ± 𝑣) ≈ 𝑓0( 𝑐 ∓ 𝑣 𝑐 ) = 𝑓0(1 ∓ 𝑣 𝑐) = 𝑓0+ 𝑓𝑑 2-2

The motion of the target object adds a doppler shift frequency fd to the reflected beam. The

equation 2-2 shows the value of fd as the vibrating object would be observed but, in the LDV

situation, the reflected light is measured. Thus, the doppler shift must be considered twice, and the overall value is:

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𝑓𝑑 = 2 𝑓0

𝑣 𝑐 = 2

𝑣

𝜆 2-3

where 𝜆 is the laser wavelength.

By adding only fd is impossible to identify the direction of the target movement thus, a

heterodyne interferometer, called also Bragg cell, is introduced. The Bragg cell adds a known frequency, normally fb = 40 MHz, to the reflected beam by generating a modulation frequency

of the fringe pattern. Thus, the final frequency of the reflected beam is: 𝑓′_{= 𝑓}

0+ 𝑓𝑏+ 𝑓𝑑 2-4

In this way, directional sensitivity is introduced, and it is now possible to detect the amplitude and direction of the motion.

The photo detector combines the reflected and the reference beam and generates a standard frequency modulated signal. It can be demodulated to derive the velocity of the target object.

2.4.2 Types of LDVs and Applications

According to the scope of the measurements and the target characteristics, different LDV are available on the market.

The easiest one is the single point vibrometer. It can measure the target vibration only in direction of the laser beam. Applied to production testing enabling fast and reliable quality check on 100% of manufactured products. In biology field, it used to collect and analyse unheard insect sounds and to assess fruit ripeness and hearing mechanisms in frogs.

The full-field vibrometers can be used to perform more complicated and accurate measurements. Belonged to this group the Scanning Laser Doppler Vibrometer (SLDV), the Multipoint Vibrometer (MLDV) and the Robovib. The SLDV enables rapid measurements of large surface and it is usually used in aerospace, automotive and acoustic field, (Polytec, 2006). The MLDV enables measurements of different surface points, while the Robovib combines a 3D Scanning Vibrometer with an industrial robot to perform

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automatic vibration analysis. The automated measuring significantly reduces the number of errors and the time required to perform the measurement.

In-plane Vibrometer, Rotational vibrometer and, Differential Vibrometer belong to the special application vibrometers group. The first one can measure perpendicular vibrations to the laser beam while the second performs measurements on rotating surface by calculating angular velocity and angular displacement. The differential vibrometer performs measurements between two points vibrating relative to each other.

Finally, a product line of microscope systems is available for microsystem applications, such as MEMS and Microstructure.

Table 2 Types of LDVs available on the market (Polytec, 2006)

In conclusion, Laser Doppler Vibrometer is widely used as an alternative to contact vibration transducers in several applications. While aerospace, mechanical, electrical and civil engineering are the most common fields, it is also applied in areas such as medical, fruit ripeness and medieval fresco maintenance, (Rothberg et al., 2017). However, no application in railway field is reported by the literature.

2.5 Thermography

Infrared thermography is a non-destructive, non-intrusive and non-contact method to map thermal patterns on the object surfaces by using an infrared camera. During the last few decades, infrared camera performances have been significantly improved, increasing their employment in monitoring and diagnostic purposes. Hence, its application cover different areas from monitoring of buildings or electronic components to artworks and composites materials, (Ibarra-castanedo and Maldague, 2013).

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Two different approaches can be followed to perform infrared inspections, the active and the passive approach. The first one is the most straightforward. Material or structure tested is naturally at different temperature than the environment, camera measures radiations and results are shown on a screen, (Titman, 2001). The passive approach is usually qualitative, and it may be useful to find discontinuities in materials only by observation, without any further analysis. Typical applications are monitoring of electric components, humidity or insulation inspections in buildings, (Hart, 1992).

On the other hand, the active approach uses an external source to stimulate a thermal contrast on the tester. Thermo-physical properties are different in defective and non-defective areas. Thus, by using an energy source located either on the same side (in reflection) or on the opposite side (in transmission) of the camera, a measurable thermal contrast will be produced. Moreover, being the external stimulation completely controlled, it is also possible to apply a quantitative analysis. Generally, the source configuration is chosen according to the presumed location of defects and eventual environment limitations. The active approach tends to be more costly and time-consuming but it enables more accurate evaluations and reliable results,(Hart, 1992).

2.5.1 Excitation Sources in Active Thermography

An advantage of thermography analysis is that any kind of energy can be used to produce a thermal contrast between the object and its defects. Heating source can be classified into 4 groups.

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Figure 11 Types of excitation sources in active thermography (Ibarra-castanedo and Maldague, 2013)

There is no excitation source universally adopted for every situation. It is usually selected according to the problem at hand. However, there are some source characteristics that can help in this choice.

Repeatability, typical for pulsed thermography, is needed to compare results of the same sample; uniformity, to reduce thermal stimulation which can create hot or cold spots interpreted as defects; timeliness, to have an adequate synchronisation between excitation source and image acquisition, particularly important for quantitative analysis; and duration which should be set according to the material properties, (Maldague, 2001). Several active thermography techniques have been developed by using different heating sources, such as lock-in thermography, pulsed and pulsed phase thermography and vibro-thermography, (Bagavathiappan et al., 2013). According to the experiments needs, pulsed thermography was selected.

2.5.2 Pulsed Thermography

In pulse thermography, a photographic flash is employed to increase the surface temperature of the specimen and, by diffusion, the thermal front propagates under the surface. For specimens without internal flaws, the surface temperature decreases uniformly. On the other hand, discontinuities in the material change the diffusion rate producing abnormal patterns

Optical Light is reflected to the target where it is transformed to heat and propagates by conduction The most common configuration is with photographic flashes, also known as pulsed thermography M echan ical Transducers are used to generate ultrasound waves into the specimen and create heat by friction In d u ctio n A coil is used to generate an eddy current in the specimen The application is limited to conductive materials _Micro wav e Microwaves are injected into the specimen

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at the surface, such as hot or cold spots. If the abnormalities are large enough they will be highlighted by IR cameras, (Ibarra-castanedo and Maldague, 2013).

For a homogeneous, semi-infinite sample, the time-dependent temperature surface in response to an instantaneous heat pulse is, (Shepard, 2007):

𝑇_{𝑠𝑢𝑟𝑓}(𝑡) − 𝑇_{𝑠𝑢𝑟𝑓}(0) = 𝑄 √𝑘𝑝𝑐𝜋𝑡

2-5

Where Q (J/m2_{) – input energy per unit area, T}

0 - initial temperature, k [W/mK]- thermal

conductivity1_{, 𝜌 [kg/m}3_{] - material density, c [J/kgK]– specific heat.}

When a temperature-time plot is generated in the logarithmic domain, a linear profile with a slope of -0.5 is obtained. If an internal damage is present, a deviation in the plot occurs, as shown in Figure 12.

Figure 12 Temperature-time plot of the heat propagation into the material (Sri Addepalli, Yifan Zhao, 2017)

Data acquisition for pulsed thermography is fast and straightforward. As shown in Figure 13, two flashes heat up the specimen and the thermal change on the surface is recorded with the IR camera as a 3D matrix (x,y,t),(Figure 14.a). According to 2-5, the temperature decreases as t-1/2_{, except for defective areas where the rate is different (Figure 14.b).}

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Figure 13 IR camera set-up (UKEssay, 2013)

Figure 14 Data recorded by IR camera

In order to explain the relationship between defect depth and time simplified by 2-5, several qualitative and quantitative techniques have been developed, (Ibarra-castanedo and Maldague, 2013).

2.5.2.1 Advantages and Limitations

Pulse thermography, as other NDT techniques, has strengths and weaknesses. The main advantages and disadvantages are listed below.

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2.5.3 Applications

Over years, more sophisticated IR camera, computer and software have been developed, increasing the fields where infrared thermography is applied. As shown in Figure 17, the passive approach is mainly applied for condition monitoring and medical application while, the active approach for defect detection and characterisation. For this reason, the active approach was selected.

pulsed

thermography has high inspection rate and the visual outputs enable immediate interpretation Velocity any contact is needed between the system and the object tested. This enables measurements where accessibility is low. Contactless differently from other NDT system such as Radiography, harmful radiations are not involved. However, the powerful flash used requires eyes protection.

Security

for pulsed active approach is difficult to obtain uniform thermal stimulation of large surface but it works well for limited areas

Non uniform

heating _{thermal losses can}

cause contrasts that can be easily confuse with defects Thermal losses IR cameras can be more expensive than other NDT systems such as, visual inspection or some ultrasound devices. However, it has has competitive cost compare to the eddy current systems.

Cost

Figure 15 Advantages of pulsed thermography

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Figure 17 Applications of passive and active thermography, (Bagavathiappan et al., 2013)

2.6 Conclusion

In conclusion, different NDT techniques are currently employed for rail inspection and each of them has different characteristics and performance. However, only few of them have the capability to detect defects on the rail foot but, their reliability is still not satisfying. Indeed, this area is very difficult to inspect and techniques usually applied to rail head and web are not enough accurate for foot damages. In accordance with Network Rail, two new NDT systems have been selected for this purpose. Indeed, Laser Doppler Vibrometer and Infrared thermography will be experimentally evaluated, and their effectiveness assessed.

3 Methodology

The methodology followed to carry out the experiments includes 4 steps. The 4 phases ensure on-time and in-full delivery of the objectives and consists of set-up measurement systems, sensitivity analysis on metal plates, experiments on metal I-beams simulating the rail, and robotic inspection requirements. Moreover, a Gantt-chart, Appendix A_A.1, was created by using Microsoft Project in order to facilitate the methodology and the objectives achievement.

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3.1 Measurement Systems Set-up

The first methodology step aims to plan the equipment needed to perform the laboratory experiments. Many different configurations are available for the experimental set-up and, the best solution was chosen taking into account company requirements, final application on rail and its environmental limitations.

3.1.1 Laser Doppler Vibrometer Set-up

The system configuration chosen consists of 3 major items: an excitation mechanism, a LDV to measure the parameters of interest and an analyser to extract the desired information.

Figure 18 LDV set-up • Equipment components • General configuration • Data acquisition Measurement Systems Set-up • Set-up parameters • Experiments configuration and preparation • Laboratory tests Sensitivity Analysis

on Metal Plates • Set-up parameters • Experiments configuration

and preparation • Inspection of selected

defect (depth, size, location)

Experiments on Metal I-beams

• Robotic arm movements • LDV and IR camera

requirements

Robotic Inspection Requirements

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a) Laser Doppler Vibrometer.

The single point LDV selected to carry out the experiments is the Polytec PDV-100. It is portable and practical thanks to its tripod configuration. It measures vibrational velocities with a frequency range of 0 to 22 kHz, and resolution of 0.02 µm/s. The stand-off distance is variable between 0.2 m to 30 m. The visible and eye-safe laser beam emitted does not require any security measures.

b) Analyser.

The function of this item is to extract the necessary information from the signal acquired with the laser. The analyser chosen is the Polytec VibSoft package which manages data acquisition, signal decoding, function generator and data display. The data acquisition board is installed on the computer and a cable connects the latter directly to the laser.

c) Excitation Mechanism.

The excitation used is an impact hammer. There are several sizes of impact hammers with weight in a range between few grams and 2 kg. Each of them has a set of different tips and heads which are needed to modify the frequency and force level applied to the structure. Within the impactor, there is a load cell or force transducer which provides the value of the impact force applied. However, a disadvantage of this excitation method is the difficulty in ensuring always the same impact in terms of magnitude, position and orientation.

3.1.1.1 Data Acquisition

The excitation force applied to the structure and the vibration response are, respectively, generated and acquired as signals. The function used by the software to correlate the signal in output with the one in input is the frequency response function (FRF).

𝐹𝑅𝐹 = 𝐻(𝑤) = 𝑋(𝑤) 𝐹(𝑤)

3-1

Where X(w) and F(w) are respectively the Fourier transform of the output and the excitation in input.

In order to evaluate the reliability of the measurements, the coherence function y2_{was used.}

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𝑦2 ₌ |𝑆𝑓𝑥(𝑤)| 2

𝑆_𝑓𝑓(𝑤)𝑆𝑥𝑥(𝑤)

3-2

Where 𝑆𝑓𝑥(𝑤) is the Cross-spectral density between f and x, and 𝑆𝑓𝑓(𝑤) and 𝑆𝑥𝑥(𝑤) the

autospectral density of f and x respectively.

0 < 𝑦2 < 1 3-3

When y2_{is almost 1, the system response is mostly given by the excitation force in input. On}

the other hand, when y2_{is closer to 0, the response is mostly given by noise and the}

measurement cannot be consider reliable.

3.1.2 Infrared Camera Set-up

The infrared camera setting includes 3 fundamental components: a control unit, an infrared camera and an excitation source, as shown in Fig. 19

Figure 19 IR camera set-up

a) Infrared Camera

The infrared camera used is the FLIR SC7000MB; InSb cooled detector. It has an image resolution of 640 × 512 and a temporal resolution of 20 mK. The acquisition frame rate is 50Hz and it can acquire up to 625 frames.

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b) Excitation Source

The excitation source used are two flash lamps which emit a flash with length of 10 milliseconds in reflection configuration.

c) Control Unit

The control unit synchronises the triggering of the flash lamps and the data acquisition. The software chosen to display and analyse the images is Mosaiq v4. It allows filtering the images in order to remove the background noise.

3.1.2.1 Data Acquisition

Thermographic Signal Reconstruction (TSR) algorithm was used to filter the thermal images and reduce the noise level. The time derivatives of the logarithmic temperature decay were particularly useful in discriminating between defective and sound points. In fact, with the TSR method, pixels that deviate from linearity in their logarithmic time evolution are enhanced by differentiation, and easily identified by their 1st_{and 2}nd_{derivative. Consequently, results were}

shown by using raw thermal images, raw thermal plots, 2nd_{differential images and 2}nd

differential plots.

Results obtained by using this technique are low influenced by the initial temperature of the samples. Indeed, the IR camera can be set-up with different values accordingly to the initial temperature and results are obtained looking at the difference between 𝑇𝑠𝑢𝑟𝑓(𝑡) and

𝑇𝑠𝑢𝑟𝑓(0), as shown in 2-5.

3.2 Sensitivity Analysis on Metal Plates

The sensitivity analysis was carried out on metal plates in order to understand the principles of the new technology and to perform a pre-evaluation of the NDT systems. In fact, complexity of the measurements and results increased switching from metal plates to I-beams. Detailed systems configuration, set-up and procedures changed between plates and I-beams consequently, a specific planning phase was needed for each step.

Metal plates (150 x 150 x 8 mm) tested were shown in Figure 20. Each plate was obtained by cutting a big metal plate (300 x 300 x 8 mm) in 4 parts. Thus, all specimens are identical and results comparable. Holes and slots, representing typical rail flaws, were agreed with Network Rail. The drawings of the damages were created by using SolidWorks. Subsequently,

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Figure 20 Plates tested with LDV and IR camera; 1st _{- sound plate; 2}nd_{– holes with different}

dimensions; 3rd_{- slot 50x10x8mm; 4}th_{-slot 25x10x8mm}

3.2.1 LDV Tests

Measurements with the LDV were performed on all plates. An impact hammer with mass 7g was used to excite the specimen and its response was collected in a frequency range between 0-5 KHz. To reduce the noise and the small repeatability of the impact force, each measurement was averaged by repeating it 4 times. To isolate the laser from the floor vibrations, its tripod was placed on a vibration isolation mat. The specimens were also supported by a professional isolation foam squares provided by Farrat UK ltd in order to recreate the grounded condition. This condition was chosen taking into account the LDV’s

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future use as rail inspection method. Temperature of the laboratory was 20° during all the experiments. The laser pointed the left edge of the plate. Several points of each plate were hit in order to identify the best response and compare the signals. The locations of these points (in yellow) were found by dividing each plate in 9 equal squares as shown in Figure 21.

Figure 21 Points hit with the hammer in each plate

3.2.2 IR Camera Tests

Measurements with the IR camera were performed on the sound plates and on the plate with holes. Initial temperature of material and environmental conditions were the same for each measurement. The IR camera was perpendicular to the specimen surface and the latter was spread with black coating to improve the surface emissivity. For each measurement a sampling rate of 50 Hz was used and a total of 490 frames were acquired, equivalent to 10 s data length. Each measurement was repeated several times to ensure the validity of results.

X X X X X X X X X X X X A B C 1 2 3 4

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Holes on the second plate have different sizes and depths, as shown in Figure 22 and Table 3, and, as output of this methodology step, those detected by the infrared camera were identified.

Figure 22 2nd _{plate- Yellow holes- opposite side of the camera; Red holes- front side of the camera}

Hole Diameter (mm) Hole Depth (mm) Depth behalf surface (mm)

1 8 6 2 2 8 4 4 3 8 2 6 4 6 6 2 5 6 4 4 6 6 2 6 7 4 6 2 8 4 4 4 9 4 2 6 10 2 6 2 11 2 4 4 12 2 2 6 13 1 4 - 14 1 6 -

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Figure 23 Plate with holes- red arrows represent the depth behalf surface of the holes

To perform the measurements, the 2nd_{plate was reversed, and multiple thermal images were}

taken from the back side. In fact, it was agreed with Network Rail that damages are likely to happen on the bottom side of the rail foot.

As output of the sensitivity analysis, a first evaluation of the LDV and IR camera was assessed, and detectable and non-detectable defects were classified. The same procedure was then used to perform measurements on I-beams and this represented the next methodology step.

3.3 Experiments on I-Beams

Due to the time constrain, logistic issues and a Network Rail’s delay in approving sizes and locations of the defects to be machined, a real rail section could not be tested. Consequently, 3 I-beams (1000x89x153), simulating the rail, were tested and the NDT techniques effectiveness completely assessed. The specimens were obtained by cutting a longer I-beam (3000x89x153) in 3 parts and machining a slot on the 2nd_{and several holes on the 3}rd_I-beam,

Figure 24.

Figure 24 I-beams tested with LDV and IR camera; 1st _{– sound beam; 2}nd_{– slot (35x10x8); 3}rd_{- holes}

with different dimensions 1

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3.3.1 LDV Tests

Measurements with the LDV were carried out on all the I-beams. An impact hammer with mass 1kg was used to excite the specimen. Same frequency range and professional carpet were used. Two professional foams were located at the end of both side of the I-beam to simulate the rail sleepers. The laser pointed the bottom of the beam, next to the slot. The I-beam was divided in 5 sections and the hammer hit each of them.

3.3.2 IR Camera Tests

Experiments were carry out on the I-beam with holes. In order to acquire images from the opposite side of the holes, the I-beam was overturned and inclined by 45° as shown in Figure 25.

Figure 25 IR camera vs I-beam configuration

For each measurement a sampling rate of 50 Hz was used and a total of 990 frames were acquired.

Dimensions (Table 4) and locations (Figure 26) of holes were discussed and agreed with Network Rail. These represent defects that are critical to be detected with current techniques, such as ultrasonic systems.

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Figure 26 Holes location in mm on the 3rd_I-beam

Hole number Diameter (mm) Hole Depth (mm) Depth behalf surface

(mm) 23,26,29,32 10 5 3 22,25,28,31 5 5 3 21,24,27,30 3 5 3 8,12,16,20 20 3 3 7,11,15,19 10 3 5 6,10,14,18 5 3 5 5,9,13,17 3 3 5 2,3,4 3,5,8 10 5,4,3 1 3 20 5

Table 4 Holes dimension on the 3rd_I-beam

Holes 1,2,3 and 4 are located under the projected area of the web while the others started from the edge and progressively get closer to the middle of the I-beam. Holes location can modify the effectiveness of the IR camera. So that, locating holes in different position, the entire area representing the rail foot was tested and, the system capability completely assessed.

In order to have images of the entire I-beam, it was divided in 5 areas and multiple shots of each of them were acquired.

As output of the last methodology step, holes detectable and non-detectable were classified and the IR camera effectiveness completely assessed.

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3.4 Robotic inspection requirements

The final objective of the Shift2Rail project is the development of an autonomous rail vehicle to undertake inspection and repair operations with an operator supervising remotely, (Catapult, 2018). For this purpose, an initial evaluation of the opportunity to include the LDV and IR camera on a robotic arm was carried out. Robotic arm movements and technology requirements to perform the inspection were defined and the robotic interface specification were listed in collaboration with ABB.

4 Results

4.1 LDV Results

4.1.1 Plates

These experiments were based on the concept that the resonance frequencies of the material change when a defect occurs.

As explained in the methodology, several points of the plate were tapped, and several material responses were collected. Analysing the signals acquired, all points produced valid results and no one of them was identified as more responsive to the impact. Consequently, only results of point A1 were shown in the graphs below while, other results can be found in Appendix B_B.1

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Figure 27 1st_{plate- FRF of A1 (sound material)}

Figure 28 1st_{plate- coherence of A1 (sound material)}

As shown in Figure 27, the FRF of the sound material has 3 peaks corresponding to 1139Hz,

0 5 10 15 20 25 30 35 40 45 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Ma gn itu d e m/s /N Frequency HZ Sound plate 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e Frequency HZ

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between 0-4 kHz, ensuring the reliability of the results obtaining. This function assumes values closer to zero after 4kHz, demonstrating that higher frequencies were not excited by the impact hammer. For this reason, only a frequency range between 0-4kHz was considered when signals were compared.

Figure 292nd_{plate- FRF of A1 (holes)}

0 5 10 15 20 25 30 35 40 45 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Ma gn itu d e m/s /N Frequency HZ

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Figure 30 2nd_{plate- coherence of A1 (holes)}

Figure 29 shows 3 peaks at frequencies of 1143Hz, 2064Hz and 2922Hz. Values of the coherence function (Figure 30) ensure the result’s reliability but, no relevant difference with the sound plate was highlighted. Hence, the dimension of holes is not enough large to influence the resonant frequencies. Consequently, they were not detected by the laser. Thus, other two plates with larger slots were tested.

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e Frequency HZ

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Figure 31 3rd_{plate- FRF of A1 (slot 50x10x8)}

Figure 32 3rd_{plate- coherence of A1 (slot 50x10x8)}

Plate with slot

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e Frequency HZ

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As displayed in Figure 31, there are 4 relevant peaks at frequencies of 895Hz, 1480Hz, 1970Hz and 2746Hz. Values of the coherence function in Figure 32 are still good hence, the largest slot modified the resonance frequencies of the plate, increasing the number of peaks and changing their frequencies.

Results became very clear when the FRFs of the 3 plates were compared on the same graph, (Figure 33).

Figure 33 Comparison of FRF of 3 plates-point A1

Finally, a smaller slot (25x10x8) was tested and results shown below.

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Figure 34 4th_{plate- FRF of A1 (slot 25x10x8)}

Figure 35 4th_{plate- coherence of A1 (slot 25x10x8)}

0 5 10 15 20 25 30 35 40 45 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Magnitu d e m /s /N Frequency HZ

Plate with half-slot

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Magnitu d e Frequency HZ

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In Figure 34, 4 peaks are achieved at 1078Hz, 1986Hz, 2688Hz and 2827Hz. By comparing the FRFs of the sound plate and those of the plates with slot, can be conclude that both the slots change the resonance frequencies and can be detected by the LDV, Figure 36.

Figure 36 Comparison of FRF of 3 plates-point A1

Considerations made for A1 are valid for all the other points and, the same conclusion was drawn. While differences between the sound material and plates with slot are extremely evident in terms of peak’s number and frequency, all values related to the 1st_{and the 2}nd_plate

are very close, and no differences can be detected. Thus, the laser is not able to detect this type of defect.

A1

Peak Sound (1st_plate) _{Holes (2}nd_plate) _{Slot (50x10x8)}

(3rd_plate) Slot (25x10x8) (4th_plate) 1° 1139 Hz 1143 Hz 895 Hz 1078 Hz 2° 2056 Hz 2064 Hz 1480 Hz 1986 Hz 3° 2905 Hz 2922 Hz 1970 Hz 2688 Hz 4° 2746 Hz 2827 Hz

Table 5 peak’s frequencies in Hz of point A1

0 5 10 15 20 25 30 35 40 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Magnitu d e m /s /N Frequency HZ

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4.1.2 I-Beams

Results were presented for point 3, that one closer to the slot. FRF of the sound and slotted I-beam were compared. Results of other points were shown in Appendix B_B.2.

Figure 37 Sound I-beam-FRF of point 3

0 20 40 60 80 100 120 140 160 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e m/s /N Frequency HZ Sound I-beam

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Figure 38 Sound I-beam-coherence of point 3

Figure 39 I-beam with slot-FRF of point 3

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Ma gn itu d e Frequency HZ

Coherence sound I-beam

0 20 40 60 80 100 120 140 160 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e m/s /N Frequency HZ

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Figure 40 I-beam with slot-coherence of point 3

Figure 41 Comparison of FRFs of point 3

As noticed in Figure 41, no remarkable difference was identified by comparing the FRFs. To complete the experiments, also I-beam with holes was tested and the results compared with

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 2 153 305 456 608 759 911 ₁₀₆₃ ₁₂₁₄ ₁₃₆₆ ₁₅₁₇ ₁₆₆₉ ₁₈₂₀ ₁₉₇₂ ₂₁₂₃ ₂₂₇₅ ₂₄₂₇ ₂₅₇₈ ₂₇₃₀ ₂₈₈₁ ₃₀₃₃ ₃₁₈₄ ₃₃₃₆ ₃₄₈₈ ₃₆₃₉ ₃₇₉₁ ₃₉₄₂ ₄₀₉₄ ₄₂₄₅ ₄₃₉₇ ₄₅₄₈ ₄₇₀₀ ₄₈₅₂ Ma gn itu d e Frequency HZ

Coherence I-beam with slot

0 20 40 60 80 100 120 140 160 180 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e m/s /N Frequency HZ

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the sound I-beam in Figure 42. However, no relevant difference of the response was observed, as expected. Consequently, defects on the I-beam were not detected by using this technique.

Figure 42 Comparison of FRFs of point 3

4.2 IR Camera Results

4.2.1 Plates 4.2.1.1 Sound Plate 0 20 40 60 80 100 120 140 160 2 158 314 470 627 783 939 ₁₀₉₅ ₁₂₅₂ ₁₄₀₈ ₁₅₆₄ ₁₇₂₀ ₁₈₇₇ ₂₀₃₃ ₂₁₈₉ ₂₃₄₅ ₂₅₀₂ ₂₆₅₈ ₂₈₁₄ ₂₉₇₀ ₃₁₂₇ ₃₂₈₃ ₃₄₃₉ ₃₅₉₅ ₃₇₅₂ ₃₉₀₈ ₄₀₆₄ ₄₂₂₀ ₄₃₇₇ ₄₅₃₃ ₄₆₈₉ ₄₈₄₅ Ma gn itu d e m/s /N Frequency HZ

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Figure 43 Raw thermal image of Sample 1-Frame 24/490 – Red, green, blue and yellow markers – sound material

Figure 44 Raw thermal plot of Sample 1

Each curve in Figure 44 represents the temperature decay of points in Figure 43 representative of the sound material. The red dot line displays the link between the frame in Figure 43 and the time. In this case, the 24th_{frame was acquired after 0.57sec. As expected, there was no}

difference between slopes, due to the fact that no damage is present in this sample.

4.00 4.60 5.20 5.80 6.40 7.00 0.02 0.06 0.24 0.82 2.84 9.78 time (sec) Time (s) In te n sit y

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Figure 45 2nd_{differential plot of Sample 1}

A second analysis was performed by plotting the values of the 2nd_{time derivative of the}

surface temperature equation. As stands out from Figure 45, all curves follow the same pattern and no significant difference was highlighted. Thus, no damaged area was identified in Sample 1 as expected.

4.2.1.2 Plate with holes

Figure 46 Raw thermal image of Sample 2-Frame 24/490

-0.30 -0.15 0.00 0.15 0.30 0.06 0.16 0.46 1.28 3.54 9.78 time (sec) Time (s) 2 d e riv a tive

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Figure 47 2d thermal image of Sample 2-Frame 24/490

Figure 46 and Figure 47 show the raw thermal and the 2nd _{derivative thermal images}

respectively. While in the raw thermal image only holes 1,2 and 4 are visible, with the 2nd

derivative also holes 5 and 7 were detected.

Figure 48 Raw thermal image of Sample 2-Frame 24/490 – Red- potential damage; Green, blue and yellow – sound material

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Figure 49 Raw thermal plot of Sample 2- Yellow, blue and green- sound material; Red - potential damage

Blue, green and yellow plots show the temperature trend of an area representative of the sound material which is considered as reference. Red marker is the temperature plot of a potential damage, hole 1. Differently from the Sample 1, the transient flow of heat from the surface into the sample is not linear for all the points. A local temperature increase at the surface, typical of damage, leads to slope changes of the red line.

4.00 4.60 5.20 5.80 6.40 7.00 0.02 0.06 0.24 0.82 2.84 9.78 time (sec) Time (s) In te n sit y

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Plotting the 2nd_{time derivative data, the time when the first slope change occurs was}

identified. It occurs at 0.08sec, as shown by the blue dot-line. A remarkable difference was highlighted between the pattern of the red line and the other curves. Indeed, three red peaks occur with different intensity and at different time compare to the yellow, green and blue peaks. A temperature contrast was delineated between the red and the reference lines (sound material). The maximum contrast, corresponding to the maximum local temperature increase in Figure 50, is identified by the black line. In conclusion, thermal images, raw temperature plot and the 2nd differential plot picked up the damage corresponding to the hole 1.

Differently from the hole 1 analysed, the raw thermal image of Figure 51 does not show any strong thermal contrast corresponding to the hole 7. However, the raw thermal plot in Figure 52 identified a slope change of the pink line, typical of damaged areas.

Time (s) 2 d e riv a tive

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Figure 51 Raw thermal image of Sample 2-Frame 24/490 – Pink- potential damage; Green, red and yellow – sound material

Figure 52 Raw thermal plot of Sample 2- Yellow, red and green- sound material; Pink - potential damage 4.00 4.60 5.20 5.80 6.40 7.00 0.02 0.06 0.24 0.82 2.84 9.78 time (sec) 7 Time (s) In te n sit y

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The 2nd_{differential plot confirmed the presence of damage, corresponding to the hole 7, by}

displaying higher peaks of the pink line. The slope change still occurs at 0.08sec. The maximum thermal contrast between the sound material and the potential defect is smaller than those of the previous holes, but still significant. In conclusion, also the hole 7 is picked up by the IR camera.

The same analyses were performed on the areas corresponding to the holes number 2,4 and 5, leading to the same conclusion. Results were displayed in Appendix B_B.3. Holes 13 and 14 did not reflect Network Rail interests consequently, were not analysed.

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Figure 54 Raw thermal image of Sample 2-Frame 24/490 – Yellow- potential damage; Green, red and blue – sound material

Differently from the previous results, the area corresponding to the hole 8 was inspected but a different conclusion was drawn.

Figure 55 Raw thermal plot of Sample 2- Blue, red and green- sound material; Yellow - potential damage

In this case, the slope of the yellow line, is very similar to those of the sound material and no slope change was identified. Even by observing the 2nd_{differential no remarkable difference}

was detected, thus the hole 8 was not picked up by the camera.

3.00 3.80 4.60 5.40 6.20 7.00 0.02 0.06 0.24 0.82 2.84 9.78 time (sec) 8 Time (s) In te n sit y

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In conclusion, according to the results, holes number 1, 2, 4, 5,7 (Figure 22) were detected by the camera. Reasons why not all the holes were detected were reported in chapter 5.1.2.

4.2.2 I-Beams

Figure 56 Holes detected by the IR camera

Figure 56 shows the 2nd_{differential image of all holes picked up with the camera. Due to the}

large number of results, only the most relevant were shown in the following section while, all the other graphs were displayed in Appendix B_B.4

The analysis carried out on the plates was applied on the I-beam as well. Differently from the plate, the defects were not readily obvious from the raw thermal images of the I-beam. Hence, only images of the 2nd_{differential was shown. In order to increase the temperature contrast}

between the defective and sound area, Figure 57, the histogram in Figure 58 was used. Each bar of the histogram represents the number of pixels of each intensity. Thus, by reducing the range of intensities shown on the image and spreading the palette colour on a reduced intensity range, temperature contrast is enhanced.

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Figure 57 2nd_{differential image of hole 32-Frame 42/990}

Figure 58 Histogram of pixel’s intensity

Figure 59 colour palette used to enhance the temperature contrast; a- wider range of intensities; b- reduced range corresponding to the orange bars in Figure 58

In fact, while with a wider range, Figure 59-a, white colour is associated to pixels with 35 different intensities, by reducing the range, only 15 intensities are allocated to white, Figure 59-b.

The raw temperature decay plot of points representative of the hole 32 and 29 was shown in Figure 60. The local temperature increase due to the defect, is clearly evident for the orange

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and green line. The temperature decays smoothly for red and violet line, representative of the sound material.

Figure 60 Raw thermal plot of I-beam with holes- Violet and red- sound material; Green – hole 32; Orange-hole 29 3.71 4.61 5.51 6.41 7.31 8.21 0.02 0.08 0.32 1.26 4.98 19.78

time (sec)Time (s)

In

te

n

sit

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Figure 61 2nd_{differential plot- Green-hole 32; Orange-hole 29}

By plotting the 2nd_{differential, differences between curves were highlighted and the presence}

of holes 32 and 29 was confirmed. Same results and consideration were obtained for holes number 26, 23 and 25,22, as shown in Appendix B_B.4.

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Figure 62 Raw thermal plot of I-beam with holes- Green and light blue- sound material; Red – hole 8; Pink-hole 12

By analysing holes 8 and 12, the slope change is well visible for the damaged areas.

Figure 63 2nd_{differential plot- Red-hole 8; Pink-hole 12}

3.73 4.25 4.78 5.30 5.82 6.35 0.02 0.08 0.32 1.26 4.98 19.78 time (sec) Time (s) 2 d e riv a tive Time (s) In te n sit y