Embed Size (px)
Citation preview
Improved Railway Track Irregularities Classification by a ModelInversion Approach
René Schenkendorf1, Beate Dutschk2, Katrin Lüddecke3, and Jörn C. Groos4
1,2,3,4 Institute of Transportation Systems, German Aerospace Center (DLR e.V.)Lilienthalplatz 7, 38108 Braunschweig, Germany
ABSTRACT
Over time railway networks have become complex systemscharacterized by manifold types of technical componentswith a broad range of age distribution. De facto, about 50 per-cent of the life cycle costs of railway infrastructures are madeup by direct and indirect maintenance costs. A remedy canbe provided by a condition based preventive maintenancestrategy leading to an optimized scheduling of maintenanceactions taking the actual as well as the expected future infras-tructure condition into account. A prerequisite is, however,that the thousands of kilometers of railway tracks are almostcontinuously monitored. Thus, a promising approach isthe usage of low-cost sensors, e.g. accelerometers and gyro-scopes, which can be installed on common in-line freightand passenger trains. Due to ambiguous data records acredible classification of railway track irregularities directlyfrom these data is challenging. Alternatively to this puredata-driven approach, in this paper a novel hybrid approachis presented. To this end, a simplified vehicle suspensionmodel is applied for the purpose of railway track conditionmonitoring by analyzing the dynamic railway track - traininteractions. The inversion of the model can be used to re-calculate the actual inputs (irregularities) of the monitoredsystem (rail surface) which have caused recorded systemresponses (dynamic vehicle reactions and acceleration data,respectively). These recalculated inputs are a sound basisof subsequent data-driven condition monitoring analyses.In this preliminary study, a classification algorithm is im-plemented to identify a simulated railway track irregularityautomatically.
René Schenkendorf et al. This is an open-access article distributed un-der the terms of the Creative Commons Attribution 3.0 United States Li-cense, which permits unrestricted use, distribution, and reproduction inany medium, provided the original author and source are credited.
1. INTRODUCTION
Roughly speaking, half of the life cycle costs of railway in-frastructures are caused by maintenance costs (Gradinariu,2008). Rail surface irregularities (e.g. Fig.1), in particular, arecritical in terms of safety and reliability and call for advancedand cost saving monitoring concepts (Haigermoser, Luber,Rauh, & Gräfe, 2015; Schenkendorf, Groos, & Johannes, 2015).Here, condition based preventive maintenance strategiesmay help leading to an optimized scheduling of mainte-nance actions taking into account the actual as well as theprospective infrastructure condition. To implement a seam-less condition monitoring of high-frequency, high-capacityrailway networks low-cost monitoring systems are manda-tory. In detail, micro electro-mechanical systems (MEMS)inertial sensors (accelerometers and gyroscopes) which havethe potential to be installed on a large amount of commonin-line freight and passenger trains (e.g. (Ward et al., 2011;Molodova, Li, & Dollevoet, 2011; Naganuma, Kobayashi, &Tsunashima, 2014; Weston, Roberts, Yeo, & Stewart, 2015;Quirke, Cantero, OBrien, & Bowe, 2016)) are promising can-didates. In this way, the gathered acceleration data may beused on a daily basis to reveal and monitor relevant railwaytrack irregularities by signal processing tools as for instancethe Continuous Wavelet Transformation (CWT). Due to am-biguous data records, however, a credible classification ofrailway track irregularities directly from low-cost sensorsacceleration data form in-line trains is challenging. Theresponse of the vehicle to the track irregularities is sensi-tive to a number of vehicle-specific and partly time varyingvehicle parameters (e.g. mass, suspension system, speed,wheel diameter, and position of the measurement device).For instance, a train passing the same critical track segmentwith different speeds produces different acceleration sig-nals. Due to these unsolved challenges pure data-drivenapproaches with low-cost sensors on in-line trains are stillnot commonly implemented in today railway monitoring
1
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
concepts. Alternatively to the pure data-driven approach, inthis work a hybrid approach is presented. To this end, a sim-plified vehicle suspension model, a.k.a. quarter-car model,is applied prior to a data-driven approach for the purposeof railway track condition monitoring. The inverse modelcan be used to recalculate the actual inputs (irregularities)of the monitored system (rail surface) which have causedrecorded system responses (dynamic vehicle reactions andacceleration data, respectively). This approach addressesthe unsolved problem how to systematically consider theserelevant and variable parameters by the detection of railtrack irregularities with low-cost sensors on in-line trains.As shown in this manuscript, these recalculated inputs area sound basis for track irregularity detection by a follow-ing data-driven approach providing, in addition, a valuableinput for subsequent studies, e.g. life cost analysis (Rama& Andrews, 2016) and prognostic charts (Saha, Goebel, &Christophersen, 2009; Cocheteux, Voisin, Levrat, & Iung,2010).
The remainder of this paper is structured as follows. In Sec-tion 2 the idea of the novel hybrid approach for rail surfacecondition monitoring is outlined. Here, in 2.1 the conceptof CWT as an essential signal processing tool is shortly sum-marized. In 2.2 the quarter-car model is derived. The basicsof a model inversion strategy to recalculate track irregulari-ties are addressed in Section 2.3. The classification strategybased on recalculated track irregularities is demonstrated in3. Finally, the conclusion is given in Section 4.
Figure 1. Corrugated rails reduce the driving comfort interms of vibration and noise while increasing the overallwear due to harsher track - train interactions.
2. HYBRID APPROACH
As previously described, the automatic detection of rail sur-face failures via low-cost sensor systems is an active researchfield within the PHM community. In literature, various ap-proaches can be found utilizing acceleration data to assessthe rail surface quality by classification and fault detection al-gorithms (Fig. 2a). These studies, however, are usually based
on idealized assumptions, e.g. an unique measurement sys-tem installed on a dedicated train with constant speed andmasses of the vehicle. Constraints which are rarely met inpractice and, in consequent, are one main reason why acredible condition monitoring of the rail surface quality failsto the present day. Alternatively, model-based approachesincorporating expert knowledge of the monitored systemcan gain helpful insight in terms of fault detection and iden-tification. When implementing model-based concepts inconjunction with data analysis ideas, a hybrid approach isformed (Lee, Ni, Djurdjanovic, Qiu, & Liao, 2006) combin-ing the advantages of both strategies. In this study, a novelhybrid approach is presented. Instead of utilizing a conven-tional model, an inverse model is applied to recalculate themodel inputs, i.e. the rail surface quality (Fig. 2b). In conse-quence, more credible results can be provided by the classi-fication of these recalculated inputs instead of analyzing theraw acceleration data directly. But before demonstrating theefficiency of the overall concept, the most relevant elementsare explained in subsequent.
2.1. Continuous Wavelet Transformation
In any data-driven concept informative features have to bederived from the analyzed data, e.g. geometrical or statisti-cal quantities calculated in the time and/or frequency do-main of the signal. In this study, frequency dependent char-acteristics assigned to the recorded track position are of fun-damental importance. Thus, the Continuous Wavelet Trans-formation is applied to extract these localized frequencyfeatures of a signal under investigation. This time-frequencyresolution provides essential information of rail surface fail-ures, associated data time spans and localization within thetrack, respectively. In detail, the mathematical definition ofthe CWT reads as:
W(a,b) := |a|− 12
∞∫−∞
f (t )Ψ∗(
t −b
a
)d t ; a,b ∈ R, a 6= 0, (1)
where f (t ) is the signal under study (e.g. acceleration data,y , or recalculated rail surface, u), Ψ(·) a so-called motherwavelet (here, a mexican hat wavelet is used), ∗ indicatesthe complex conjugate, a and b are scaling and translationparameters, respectively. For more details, the interestedreader is referred to (Teolis, 1998) and references therein.
Depending on a and b, wavelet coefficients, W(a,b), can bedetermined which reveal time-frequency patterns of a signal.These characteristics, sometimes referred as the fingerprintof a signal, can be used as an indicator of the health statusof the analyzed system, e.g. the presence or absence of railsurface failures and the condition of other railway assets aswell (e.g. (Asada & Roberts, 2013; Molodova, Li, Nunez, &Dollevoet, 2013; Cantero & Basu, 2015)). Technically, in thispaper normalized wavelet coefficients (against the signals
2
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
System(In-line Train)
Feature Extraction(Continuous Wavelet Transformation)
Fault Identification(Support Vector Machine)
(Acceleration Data)y
(Rail Surface)u
(Wavelet Coefficients)W(a,b)
(Rail Surface Failure)Failure Mode
(a) Data-driven Approach
Hybrid Framework
System(In-line Train)
Model Inversion(Inverse Simulation)
Feature Extraction(Continuous Wavelet Transformation)
Fault Identification(Support Vector Machine)
(Acceleration Data)
y(Recalculated Rail Surface)
u
(Rail Surface)
u
(Wavelet Coefficients)
W(a,b)(Rail Surface Failure)
Failure Mode
(b) Hybrid Approach
Figure 2. Compared to the conventional data-driven approach (a) the proposed hybrid approach (b) combines data-drivenconcepts with model-based ideas .
total energy Eq.(2)) are used to ensure a better comparability.
E =∞∫
−∞| f (t )|2d t . (2)
Typically, CWT is directly applied to the raw data for thepurpose of condition monitoring. In this study, however,CWT is combined with a model-based approach as outlinedin the next sections.
2.2. Mechanistic Model
msxs
xus
Driving direction
mus
ks c
kus
ms
mus
ms
mus
u(t )
Figure 3. Quarter-car model: Mechanical suspension systemdescribing the dynamic railway track - train interaction, e.g.crossing track irregularities, u(t ).
Beside the data-driven concepts, first-principle / mecha-nistic models are excellent tools to take account for expert
knowledge of the monitored system and to gain valuableinsight for a detailed diagnosis. For instance, the responseof the vehicle dynamic can be simulated by a mechanicalvehicle suspension system. In the simplest case a singleaxis movement is modeled (Fig. 3) known as quarter-carmodels (Imine, 2011; Naganuma et al., 2014). The governingequation set of this quarter-car model is presented in itsstate-space form:
x = Ax +Bu
y = Cx +Du(3)
where u ∈ Rnu and y ∈ Rny are the system inputs and theoutputs, respectively. The system states are given by x ∈Rnx .The system matrices are known as the dynamic matrix A, theinput matrix B, the output matrix C, and the feedthroughmatrix D.
In case of the quarter-car model the corresponding matrices,assuming x = [xs , xus , xs , xus ]> and y = xs , are:
A =
− c
ms
cms
− ksms
ksms
cmus
− cmus
ksmus
− (ks+kus )mus
1 0 0 00 1 0 0
(4)
B =
0
kusmus
00
(5)
C =[− c
ms
cms
− ksms
ksms
](6)
D = [0] (7)
Here, the system includes the sprung mass, ms , and theunsprung mass of the vehicle, mus , which are connected bya linear spring and damper with the stiffness coefficient, ks ,
3
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
and the damping constant, c , respectively. The rail surface isconsidered as the system input, u(t ), and is transmitted bya spring (kus ) to the unsprung mass (Fig.3). The numericalvalues of the model parameters are given in Table 1.
Table 1. Applied model parameters of a generic railway vehi-cle adapted from (Sira-Ramirez et al., 2011).
Model parameter Numerical valuems 9875 [kg ]
mus 1100 [kg ]ks 2.13×106 [N/m]
kus 1.42×108 [N/m]c 1.20×104 [N− s/m]
2.3. Model Inversion
The special feature of this study is to not directly apply thederived vehicle model for condition monitoring but its in-verse. In general, the basic concept of model inversion aimsat using the recorded output data of the system under studyand to reconstruct the underlying inputs by systems the-ory principles. Various model inversion strategies exist, see(Czop, Mendrok, & Uhl, 2011; Schenkendorf & Groos, 2015)and references therein. In this work, the inverse simula-tion approach is applied extending the original model bya feedback control loop (Buchholz & v. Grünhagen, 2007;Murray-Smith, 2011). For the purpose of illustration thestate-space model (Eq.(3)) is transferred into the transferfunction notation first, i.e. applying Laplace Transforma-tion:
G(s) = C(sI−A)−1B+D (8)
In general, the transfer function, G(s), represents the in-put/output behavior of the system:
G(s) = Y(s)
U(s)(9)
A straightforward inversion of the transfer function is inmost practical cases not feasible as the resulting inversetransfer function becomes non-causal. Alternatively, thecorresponding closed-loop system (Fig.4) results in:
G†(s) = U(s)
U∗(s)= Kc
1+Kc ·G(s)= Kc ·U(s)
U(s)+Kc ·Y(s)(10)
Kc G(s)U(s)U∗(s) Y(s)
−
Figure 4. Control loop: Inverse simulation by a proportionalfeedback strategy.
Obviously, for a large controller gain, Kc , the inverse systemcan be derived according to:
limKc→∞
G†(s) = U(s)
Y(s)(11)
(Here, Kc = 1000.) Transferred back into the time-domain,the reconstructed input reads as:
u(t ) ≈ u(t ) =t∫
0
g †(t −τ)y(τ)dτ (12)
As demonstrated in the subsequent section, these recalcu-lated inputs u (rail surfaces) are a sound basis for conditionbased maintenance, i.e. the machine-aided detection of railsurface failures triggering optimized maintenance actions.
3. DEMONSTRATION
After essential aspects of the proposed hybrid concept havebeen presented, their gainful interaction is illustrated via apreliminary simulation study and compared with the tradi-tional approach, i.e. pure data-driven analysis of acceler-ation data. First, informative features of the signal understudy have to be extracted. In this work, CWT is applied andthe resulting wavelet coefficients (Eq.(1)) are rearranged asfeature vectors. Here, each feature vector represents a tracksegment of 5 m length. These feature vectors train a classifi-cation algorithm. In subsequent, a Support Vector Machine(SVM) (Bishop, 2008) is used which, in theory, classifies newincoming data according to the underlying track quality. (Aradial basis kernel is used.) Considering the standard ap-proach, i.e. evaluating the acceleration data directly, am-biguous data records due to variation in vehicle parameters(e.g. mass, speed) make a proper classification difficult. As-suming a rail segment of 240 m length with corrugation frommeter 60 to 160, four different scenarios are modeled andanalyzed, i.e. acceleration data of the unsprung mass at lowspeed (40 km/h) and higher speed (160 km/h) compared toacceleration data of the sprung mass at low speed (40 km/h)and higher speed (160 km/h). Simulating a corrugated railsegment, i.e. a periodic pattern on the rail surface, the re-sulting CWT analysis is shown in Figs.(5a-5d). Obviously,the resulting wavelet coefficients are sensitive to the posi-tion of the installed measurement system (e.g. unsprung orsprung mass) and the traveling speed of the train (e.g. lowor high). A proper classification based on these findings ischallenging.
Alternatively, when using acceleration data to reconstructthe rail surface first the CWT analysis reveals similar pat-terns compared to the original rail surface failures. In detail,the wavelet coefficients of the original (Fig.5e) and of thereconstructed rail surface (Fig.5f) are compared. Obviously,there are no significant differences detectable. It should bestressed, that independent of the sensor position (unsprung
4
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
(a) Acceleration data of theunsprung mass, xus , at 40 km/h
(b) Acceleration data of thesprung mass, xs , at 40 km/h
(c) Acceleration data of theunsprung mass, xus , at 160 km/h
(d) Acceleration data of thesprung mass, xs , at 160 km/h
(e) Rail surface geometry of theoriginal rail segment
(f) Rail surface geometry of therecalculated rail segment
Figure 5. CWT analysis: Scalograms representing |W(a,b)| (Eq.(1)) with low, medium, and high numeric values.When analyzing acceleration data, f (t ) := y(t ) = xus (t ) or f (t ) := y(t ) = xs (t ), the wavelet coefficients vary in their values atthe different scenarios shown in (a)-(d): 1) Higher speed correlates to higher frequency contribution (c)-(d) and lower scale,respectively. 2) Acceleration data of the sprung mass show in addition a relevant eigenmode contribution of the system,see the high scale range of (b) & (d). However, evaluating the rail surface geometry alternatively results in similar waveletcoefficients for the simulated ( f (t ) := u(t )) and the recalculated rail surface ( f (t ) := u(t )) as shown in (e)-(f).
5
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
Figure 6. Rail Surface Failure Classification: Perfect Condition; Corrugated Part; Long-Periodic Irregularities
or sprung mass) or the speed (low or high) the reconstructedrail surface and the wavelet coefficients are equivalent.
In consequence, using a feature vector based on waveletcoefficients of the reconstructed and non-ambiguous inputsthe actual classification step becomes easier. For the pur-pose of demonstration, a track segment of 500 m length issimulated showing two different kinds of rail irregularities:(1) a corrugated part from meter 50 to 150; (2) long-periodicirregularities from meter 250 to 450. A properly trained SVMis used to identify these irregularities based on the recal-culated input (rail surface), i.e. incorporating simulatedsprung mass acceleration data indirectly. As illustrated inFig.6, except for the transition zones the critical parts of therail segment are correctly identified.
4. CONCLUSION
In this study it is shown how a model inversion strategy canbe usefully combined with machine learning techniquesforming a hybrid approach. In this way, the original gatheredacceleration data are first transferred back into an unam-biguous rail surface profile which is used for further analysisand classification purposes. Preliminary results of this hy-brid approach are derived by a simplified simulation study.Here, under ideal assumptions (i.e. perfect measurementdata and no process noise) the critical track segments areidentified correctly. In future, the proposed concept will beextended in the following way: (1) incorporating real (non-simulated) data; (2) extending the quarter-car model to afull car-model to distinguish between rail surface irregulari-ties and structure-borne noise; (3) applying special forms ofKalman Filtering for the purpose of model inversion and railsurface reconstruction, respectively.
ACKNOWLEDGMENT
The financial support by German Aerospace Center (DLRe.V., Cologne) under grant TrackScan is gratefully acknowl-edged.
NOMENCLATURE
A system dynamic matrixB system input matrixC system output matrixD system feedthrough matrixE energy of a signalG transfer functionG† inverse transfer functionW wavelet coefficient matrixa wavelet scaling parameterb wavelet translation parameterc damping constantf signal/data vectorks spring stiffness coefficient sprung masskus spring stiffness coefficient unsprung massms sprung mass of the vehiclemus unsprung mass of the vehicleu system inputu recalculated system inputx system statesxs position of the sprung massxs velocity of the sprung massxs acceleration of the sprung massxus position of the unsprung massxus velocity of the unsprung massxus acceleration of the unsprung massy system outputΨ mother wavelet functionΨ∗ complex conjugate wavelet function
REFERENCES
Asada, T., & Roberts, C. (2013). Improving the dependabilityof dc point machines with a novel condition monitor-ing system. Proceedings of the Institution of Mechani-cal Engineers, Part F: Journal of Rail and Rapid Transit,227(4), 322-332.
Bishop, C. M. (2008). Pattern recognition and machinelearning. Springer.
Buchholz, J., & v. Grünhagen, W. (2007). Inversion impossible.
6
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
GRIN Publishing GmbH.Cantero, D., & Basu, B. (2015). Railway infrastructure dam-
age detection using wavelet transformed accelerationresponse of traversing vehicle. Structural Control andHealth Monitoring, 22(1), 62–70.
Cocheteux, P., Voisin, A., Levrat, E., & Iung, B. (2010). Systemperformance prognostic: context, issues and require-ments. 1st IFAC Workshop on Advanced MaintenanceEngineering, Services and Technology, Lisabon, Portu-gal.
Czop, P., Mendrok, K., & Uhl, T. (2011). Application of in-verse linear parametric models in the identification ofrail track irregularities. Archive of Applied Mechanics,81(11), 1541–1554.
Gradinariu, T. (2008). Lasting infrastructure cost benchmark-ing (licb): Summary report december 2008 (Tech. Rep.).International Union of Railways.
Haigermoser, A., Luber, B., Rauh, J., & Gräfe, G. (2015). Roadand track irregularities: measuement, assessment andsimulation. Vehicle System Dynamics, 53(7), 878-957.
Imine, H. (2011). Sliding mode based analysis and identifi-cation of vehicle dynamics. Springer.
Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., & Liao, H. (2006). In-telligent prognostics tools and e-maintenance. Com-puters in Industry, 57, 476-489.
Molodova, M., Li, Z., & Dollevoet, R. (2011). Axle box accel-eration: Measurement and simulation for detection ofshort track defects. Wear, 271(1-2), 349–356.
Molodova, M., Li, Z., Nunez, A., & Dollevoet, R. (2013). Mon-itoring the railway infrastructure: Detection of sur-face defects using wavelets. Proceedings of the 16thInternational IEEE Annual Conference on IntelligentTransportation Systems (ITSC 2013), The Hague, TheNetherlands.
Murray-Smith, D. J. (2011). Feedback methods for inversesimulation of dynamic models for engineering sys-tems applications. Mathematical and Computer Mod-elling of Dynamical Systems, 17, 515-541.
Naganuma, Y., Kobayashi, T., & Tsunashima, H. (2014). Trackgeometry estimation from car-body motions of rail-way vehicle. Vehicle System Dynamics, 52(sup1), 207-219.
Quirke, P., Cantero, D., OBrien, E. J., & Bowe, C. (2016). Drive-by detection of railway track stiffness variation usingin-service vehicles. Proc IMechE Part F: J Rail andRapid Transit, in print.
Rama, D., & Andrews, J. D. (2016). Railway infrastructure as-set management: the whole-system life cost analysis.IET Intelligent Transport Systems, 10, 58-64.
Saha, B., Goebel, K., & Christophersen, J. (2009). Compari-son of prognostic algorithms for estimating remaininguseful life of batteries. Transactions of the Institute ofMeasurement and Control, 31, 293-308.
Schenkendorf, R., & Groos, J. C. (2015). Global sensititiv-
ity analysis applied to model inversion problems: Acontribution to rail condition monitoring. Interna-tional Journal of Prognostics and Health Management,6(019), 1–14.
Schenkendorf, R., Groos, J. C., & Johannes, L. (2015).Strengthening the rail mode of transport by conditionbased preventive maintenance. IFAC-PapersOnLine,48(21), 964-969.
Sira-Ramirez, H., Matamoros-Sanchez, A., & Goodall, R. M.(2011). Flatness based control of a suspension system:A gpi observer approach. 18th IFAC World Congress,Milano, Italy.
Teolis, A. (1998). Computational signal processing withwavelets. Birkhäuser.
Ward, C. P., Weston, P. F., Stewart, E. J. C., Li, H., Goodall,R. M., Roberts, C., . . . Dixon, R. (2011). Conditionmonitoring opportunities using vehicle-based sensors.Proceedings of the Institution of Mechanical Engineers,Part F: Journal of Rail and Rapid Transit, 225(2), 202-218.
Weston, P., Roberts, C., Yeo, G., & Stewart, E. (2015). Perspec-tives on railway track geometry condition monitoringfrom in-service railway vehicles. Vehicle System Dy-namics, 53(7), 1063–1091.
BIOGRAPHIES
René Schenkendorf René received hisDipl.-Ing. and Dr.-Ing. in EngineeringCybernetics from the Otto-von-Guericke-University Magdeburg, Germany in 2007and 2014, respectively. From 2007 until2012 he had been a Ph.D. student at theMax Planck Institute for Dynamics of Com-plex Technical Systems in Magdeburg, Ger-
many. From 2013 until 2016 he was with the GermanAerospace Center at the Institute of Transportation Systemsin Braunschweig, Germany. Since 2016 René is the headof the Process Systems Engineering group at the Institute ofEnergy and Process Systems Engineering at the TechnischeUniversität Braunschweig, Germany. His current researchinterests include data analysis, statistical process control,uncertainty analysis, and modeling in PHM.
Beate Dutschk Beate received her M.Sc.in Systems Engineering and EngineeringCybernetics from the Otto-von-GuerickeUniversity Magdeburg, Germany, in 2015.Since 2015 she is with the GermanAerospace Center at the Institute of Trans-portation Systems in Braunschweig, Ger-many. Her current research interests in-
clude data analysis for condition monitoring of railway as-sets.
7
EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016
Katrin Lüddecke Katrin studied electricalengineering and received both her diplomafrom the Technische Universität Braun-schweig, Germany, and her M.Sc. fromthe Royal Institute of Technology (KTH) inStockholm, Sweden, in 2006. Since 2006she is with the German Aerospace Centerat the Institute of Transportation Systems
in Braunschweig, Germany. There, she leads the workinggroup Asset and System Monitoring. Her research interestsinclude data acquisition and data analysis for vehicle-bornepositioning and condition monitoring of railway assets.
Jörn C. Groos Jörn received his diplomain geophysics from the University of Karl-sruhe, Germany, in 2007 and his Dr. rer. nat.from the faculty of physics of the KarlsruheInstitute of Technology in 2010. Since 2014he is with the German Aerospace Centerat the Institute of Transportation Systemsin Braunschweig, Germany. His current
research interests include data acquisition, data analysis,and pattern recognition for condition monitoring of railwaytracks.
8
