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# Mobile Inductive Thermography for Non-Destructive Testing of Manganese Steel Turnout Frogs: Initial field tests on the track

Christoph TUSCHL<fnref n="1" />, Beate OSWALD-TRANTA<fnref n="1" />, Clemens JUNGERT<fnref n="2" />, Melanie PÖTZ<fnref n="3" />, Sven ECK<fnref n="4" />

## Abstract

Manganese steel turnout frogs are widely utilized in the Austrian railway system due to their excellent mechanical properties. However, their coarse grain structure presents challenges for traditional non-destructive testing (NDT) methods, such as eddy current and ultrasonic testing. Active inductive thermography offers an effective solution for inspecting such austenitic materials. This study showcases the results of a first in-field test using a mobile demonstrator designed for detecting and characterizing surface defects. Defect detection is achieved through a scanning approach, while subsequent static measurements enable crack characterization, including estimations of crack depth and penetration angle.

**Keywords:** inductive thermography, crack characterization, scanning inspection, rolling contact fatigue, turnout inspection, Hadfield

## 1. Introduction

Rolling contact fatigue (RCF) represents critical damage types, affecting wheels, rails as well as turnouts. Mechanical stresses, wear due to repeated loading and frictional forces have been identified as the primary contributors to RCF. Therefore, turnout frogs, the junction where rails intersect, are especially prone to RCF damage, since all the mentioned factors are increased in these components [1,2]. As of 2023, the Austrian rail network extends over 9.748 kilometers and consists of 12.919 turnouts, highlighting the vast scale of potentially affected infrastructure [3]. Regular maintenance and inspection, especially for turnout frogs, are paramount, as undetected defects can develop into substantial failures and costly service interruptions. Non-destructive testing (NDT) techniques, such as eddy-current testing and ultrasonic testing, are essential for the accurate detection and evaluation of both surface and subsurface defects in these critical components. However, the complex geometry of turnout frogs and the integration of specific materials, such as manganese steel, present significant challenges for NDT methods commonly used in this field. Although manganese steel is favored in turnout frogs at the Austrian Federal Railways due to its exceptional impact strength and abrasion resistance, its coarse grain structure hinders the propagation of ultrasonic waves [4,5]. Consequently, alternative NDT approaches, notably induction thermography, are being explored for the reliable inspection of manganese steel turnout frogs.

Induction thermography has been extensively studied for its potential to inspect railway components [6–10]. An induction pulse induces eddy currents within a specimen, resulting in a localized surface heating of less than 5°C. Surface defects disturb the eddy currents, thereby altering the resulting temperature distribution. These subtle thermal variations are captured by a sensitive infrared camera, enabling the detection of small defects [11]. Furthermore, using proper evaluation techniques a characterization of these defects is possible. By converting the acquired image sequence into a phase image via pixel-wise Fourier transform, the method provides estimates of defect length, depth and penetration angle of surface cracks [12]. This evaluation also minimizes the influence of inhomogeneous heating and surface impurities. As a standardized non-destructive testing (NDT) approach, induction thermography is contactless, rapid, and well-suited for scanning applications, making it an ideal candidate for railway inspection [13].

<footnote n="1">¹ Chair of Automation and Measurement, Technical University Leoben, Leoben, Austria, christoph.tuschl@unileoben.ac.at, beate.oswald@unileoben.ac.at</footnote>

<footnote n="2">² ÖBB-Infrastruktur AG, Wien, Austria, clemens.jungert@oebb.at</footnote>

<footnote n="3">³ voestalpine Railway Systems GmbH, Zeltweg, Austria, melanie.poetz@voestalpine.at</footnote>

<footnote n="4">⁴ Materials Center Leoben Forschung GmbH, Leoben, Austria, sven.eck@mcl.at</footnote>
In this article, we present the results of a field study in which a mobile demonstrator was used for the non-destructive testing of manganese turnout frogs in a main line in Austria using inductive thermography. Defect detection is performed through three consecutive scans covering both wing rails and the frog nose. Detected defects are subsequently characterized using localized static measurement. For this study, the evaluations were conducted immediately after the measurement in track at a safe distance from the railway track. The primary objective was to assess the performance of the mobile demonstrator under operational conditions and to validate the feasibility of employing a mobile inspection rig for on-site diagnostics. Additionally, the study examined the duration required to close the track for testing, a critical parameter for minimizing operational disruptions.

The paper is structured as follows: First, the mobile demonstrator and its components are described. The subsequent section describes the evaluation techniques employed for detecting and characterizing defects on the turnout frogs. This is followed by a description of the on-site testing procedures and a presentation of the resulting data. Finally, the paper concludes with a summary and a discussion of these initial test outcomes.

## 2. Setup of the mobile demonstrator

The following section outlines the individual components of the mobile test rig. The setup consists of two main structures: a mobile platform equipped with a frame for the measurement equipment, and a trolley that houses the control system. The mobile platform is deployed on the track to conduct measurements, whereas the control system is stationed adjacent to the track for initiating and evaluating the measurements. This split into two structures was implemented to optimize weight distribution for better handling on the track. A high-current power generator supplies the necessary energy for in-field measurements. Furthermore, a positional reference target is needed for the evaluation of scanning measurements, which will be also described in this section.

### 2.1 Mobile platform

The mobile platform consists of an extendable trolley and a frame construction used for positioning the infrared camera as well as the inductor (see Figure 1.a). During operation, the induction generator (IFF UNICUM [14]) is mounted on the trolley and the microbolometer infrared camera (FLIR A615 [15]) as well as the inductor are installed on a movable sliding frame. This configuration allows the measuring system to traverse between the left and the right-hand rails (see Figure 1.b). During an in-field measurement, the camera and inductor are covered by a wooden box to prevent reflections from sunlight and other sources of heat. The infrared camera is set to a frame size of 240 × 640 pixels to increase the framerate to 100 Hz. This is necessary to get a sufficient number of images during scanning measurements. Despite this increase to a framerate of 100 Hz, the camera with a detector time constant of 8 ms still operates below its effective upper framerate limit. The distance between the rail and the infrared camera while scanning results in a spatial resolution of 0.35 mm per pixel. For static measurements this distance is reduced, increasing the spatial resolution to 0.2 mm per pixel. The inductor is placed contactless above the rail surface to prevent damage while scanning. The wiring of all devices is combined into a single strand that extends to the control system.
<figure><img src="image_3.png" /><figcaption>Figure 1. (a) Image of the cart for the mobile platform; (b) Assembly of the mobile platform</figcaption></figure>

## 2.2 Control system

The control system consists of a modern PC and a programmable logic controller (PLC), both mounted on a movable frame (see Figure 2). The PLC is responsible for controlling the induction generator and triggering the camera to start recording. On the PC two custom-developed MATLAB-based user interfaces are employed. One for setting up and recording a measurement and the other for evaluation of recorded image sequences.

<figure><img src="image_4.png" /><figcaption>Figure 2. Front and back view of the frame containing the control system</figcaption></figure>

## 2.3 Reference target

To reference subsequent frames of a recorded scan a positional reference target is used, which is also recorded during the scanning measurement (see Figure 3). Two steel strips, each 20 mm wide, were uniformly painted with black paint to provide a high-contrast background. A reference pattern, so-called AprilTags [16], was then removed from this painted surface via laser engraving. Each individual AprilTag measures 15 × 15 mm, with a spacing of 15 mm between adjacent tags. The steel strips were attached to square aluminum tubes to ensure the stability of the target.

<figure><img src="image_5.png" /><figcaption>Figure 3. Reference target for scanning measurements: Two metallic rulers with AprilTags</figcaption></figure>
### 3. Applied evaluation methods

In both scanning and static measurements, the recorded image sequences are processed to generate phase images. This evaluation technique uses a pixel-wise Fourier transform to compute phase values from the temperature profile at each pixel. The use of phase images effectively minimizes the influence of inhomogeneous heating and surface irregularities, which is a significant advantage for the inspection of rails and turnouts. For scanning measurements, an additional pre-processing step restructures the image sequence to compensate for the motion between frames. This section only gives a short description of this procedure, as a detailed description can be found in previous works [17]. In the context of static measurements, phase images are utilized to estimate both the depth and penetration angle of detected defects. This section will give an introduction to our approach.

#### 3.1 Generating a phase image from a scanning measurement

Scanning is carried out by an operator manually pushing the mobile platform along the track. Accurate determination of the scanning speed is crucial for evaluating the thermal response over time at specific positions on the specimen. In the setup presented here, AprilTags, a visual fiducial system, are used to register the shift between frames. With known spatial resolution the speed between each frame can be computed. Furthermore, the calculated speed is used to compensate for motion blurring as it is described in Ref. [18]. Finally, the entire image sequence is spatially re-organized, so that the specimen seems to be stationary. In this re-organized sequence, the Fourier transform to phase image is applied, similarly to static measurements. A scheme of the process is shown in Figure 4.

<figure><img src="image_6.png" /><figcaption>Figure 4. Postprocessing scheme for calculating a phase image from a scanning measurement</figcaption></figure>

#### 3.2 Estimation of depth and penetration angle for surface defects

Ref. [8] demonstrates that the phase image of a surface defect contains depth information. In that study, a line profile through the defect in the phase image is analyzed; this reveals changes in the response when the depth or the penetration angle is altered. Therefore, studies have been performed on manganese steel to understand these changes. These studies use a combination of simulations and reference samples, which are used to verify depth and penetration angle estimations on real defects (see Figure 5) [19].
<figure><img src="image_7.png" /><figcaption>Figure 5. Simulations and measurements on a calibration target are used to calibrate depth and penetration angle estimation</figcaption></figure>

## 4. In-field measurement campaign

Multiple field tests of this demonstration device were conducted to improve the setup and test its reliability. The following sections describe the procedure and results of one such test, conducted on September 20th, 2024 at a high-traffic turnout near the train station in Peggau-Deutschfeistritz, Austria. The measurements were carried out under the supervision of ÖBB-Infrastruktur AG, which also coordinated the necessary track closures. The aim was to detect and characterize surface defects on a turnout frog which includes both wing rails as well as the frog nose (see Figure 6).

<figure><img src="image_8.png" /><figcaption>Figure 6. Schematic of a turnout frog</figcaption></figure>

First, the setup was delivered next to the railway track and connections between the mobile platform and the control system were subsequently established. After a check of the system the first track closure was initiated to perform scanning measurements for defect detection on both wing rails and the frog nose. For preparation, the rails were spray painted with chalk paint to enhance the thermal contrast and the calibration target was positioned adjacent to the first rail to be scanned. Once the mobile platform was lifted onto the track, the infrared camera along with the inductor were accurately positioned over this rail (see Figure 7.a). In Figure 7.b a schematic sketch for the final scanning setup is shown. More details on the cameras field of view while scanning is given in Ref. [17]. With this, the device was set up for the first measurement (see Figure 7.c). For the following two necessary scans, camera and inductor were re-positioned accordingly. After completing these scans, the mobile platform was removed from the track, and the track closure ended after approx. 20 minutes. Evaluation of the scanning measurements was performed at a safe distance adjacent to the tracks.
<figure><img src="image_9.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>(c)</figcaption></figure>

<caption>Figure 7. a.) Positioning of camera and inductor above the spray-painted rail; b.) Schematic sectional view for the position of camera, inductor and calibration target; c.) Performing a manual scan via pushing the platform on the track</caption>

After identifying positions on the turnout frog with RCF damage, a second track closure was initiated to perform static measurements. Therefore, the mobile platform was again lifted onto the track and camera and inductor were positioned above each defect (see Figure 8.a and b). After the measurements the platform was again removed from the track, the second track closure ended and the evaluation was performed on-site next to the track.

<figure><img src="image_12.png" /><figcaption>(a)</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(b)</figcaption></figure>

<caption>Figure 8.a.) Positioning of camera and inductor above a RCF defect; b.) Top view of the measurement setup</caption>

# 5. Measurement results

Both scanning and static measurements were evaluated immediately after leaving the track. The primary objective of the scanning measurements is to detect and localize surface defects, while subsequent static measurements are employed to estimate crack depth and penetration angle.

## 5.1 Results of scanning measurements

Figure 9.a presents the phase image resulting from the scan performed on the frog nose. The manual scan was conducted at speeds ranging from 40 to 120 mm/s, covering approx. 1200 mm. Due to the variation in speed, each vertical line of pixels differs in the amount of data for the calculation of corresponding phase values. These differences are also visible as vertical stripes in the resulting phase image (more details in Ref. [17]). At the bottom of the image, the AprilTags used for referencing the frames are visible (see Figure 9.b). The scan shows two regions of interest with a clear indication of defects (marked in red in Figure 9.b), a larger defect structure close to the tip of the frog nose and a single defect further away. As the spatial resolution is known, the distance between the tip of the frog nose and the ROIs is determined during evaluation, so that static measurements can then be performed at these locations.
<figure><img src="image_14.png" /><figcaption>Figure 9. a.) Phase image computed from the scanning measurement of the frog nose; b.) Phase image with overlay, marking ROIs</figcaption></figure>

**5.2 Results of static measurements**

Static measurements were conducted on the larger defect structure close to the tip of the frog nose. For detailed characterization, the phase values along a line profile through the center of the defect (indicated by the red line in Figure 10.a) were compared to simulation results. As shown in Figure 10.b, the simulations of a defect with a depth of 0.5 mm and a penetration angle of 45° (blue plot) and a defect with a depth of 0.75 mm and a penetration angle of 30° (green plot) closely match the measured data. Based on this comparison, the defect depth is estimated to be between 0.5 mm and 1 mm, with a penetration angle between 30° and 45°.

<figure><img src="image_15.png" /><figcaption>Figure 10. a.) Phase image of the frog nose including a line profile through a single defect; b.) Comparison of the line profile with two simulations</figcaption></figure>

**6. Summary and conclusion**

In-field measurements were conducted using a mobile NDT system designed for inspecting manganese turnout frogs via inductive thermography. Scanning measurements are used to locate defects on the running surfaces of both wing rails and the turnout frog, while subsequent static measurements are utilized to characterize the detected defects.

This first in-field test of our NDT system highlights the potential of inductive thermography as a viable NDT method for the maintenance of rails and turnouts. As a contact-free technique, it facilitates the inspection of geometrically complex components, such as turnout frogs, without changes in the setup.

Future work will focus on the improvement of the defect characterization methodology. This includes broadening simulations to cover a wider range of defect geometries and increasing the number of test specimens to validate the simulation results. Additionally, reducing the system's weight is essential to enable its deployment for further tests also in remote locations.
**Author contribution**

**Christoph Tuschl** conducted the measurements, performed the data evaluations, and drafted the initial version of the manuscript.

**Beate Oswald-Tranta** supervised the thermographic aspects of the project, contributed expertise in thermographic evaluation techniques, and critically reviewed the manuscript.

**Sven Eck** provided overall project supervision, contributed knowledge of railway materials, coordinated the preparation of specimens with defined artificial defects for validation purposes and proof-read the final manuscript.

**Clemens Jungert** supplied damaged, out-of-service turnout frogs for calibration of the measurement setup and coordinated the in-field testing campaign described in this work.

**Melanie Pötz** arranged for rail specimens to be used in the creation of calibration specimens and organized post-processing steps to refine specimens containing artificial defects using a full-scale rail-wheel test rig.

**Acknowledgement**

The authors gratefully acknowledge the financial support by the IC-MPPE Company Project Partners ÖBB-Infrastruktur AG and voestalpine Railway Systems GmbH as part of the K2 Center "Integrated Computational Material, Process and Product Engineering (IC-MPPE)" (Project No 886385) under the scope of the COMET program.

This program is supported by the Austrian Federal Ministries for Economy, Energy and Tourism (BMWET) and for Innovation, Mobility and Infrastructure (BMIMI), represented by the Austrian Research Promotion Agency (FFG), and the federal states of Styria, Upper Austria and Tyrol.

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