Condition Monitoring of Rail Infrastructure and Rolling Stock using

Acceleration Sensor Data of on-Rail Freight Wagons

Thomas Otte

, Andres Felipe Posada-Moreno

, Fabian Hübenthal

, Marc Haßler

Holger Bartels

, Anas Abdelrazeq

and Frank Hees

RWTH Aachen University, Aachen, Germany

Deutsche Bahn (DB) Cargo AG, Frankfurt am Main, Germany

Keywords: Pattern Recognition Application, Rail Freight Transport, Real-world Case Study, Shock Data Analysis,

Condition Monitoring, Infrastructure Monitoring, Fleet Monitoring.

Abstract: In various industry sectors all over the world, the ongoing digital transformation helps to unlock benefits for

individual components, involved processes, stakeholders as well as the overarching system (e.g., the national

economy). In this context, the rail transport sector can particularly benefit from the increased prevalence of

sensor systems and the thereby increased availability of related data. As rail transport, by nature, is an

integrated transport mode that contains both freight and passenger transport within the same transport network,

benefits achieved for the service quality of freight transport also lead to improvements for passenger transport

(e.g., punctuality or uptime of rolling stock). This technical paper presents a method to monitor the condition

of the existing rail infrastructure as well as the rolling stock by obtaining insights from raw sensor data (e.g.,

locations and acceleration data). The data is collected with telemetry-units (i.e. multiple sensors integrated

with a telematics device to enable data transmission) mounted on a fleet of on-rail freight wagons. In addition,

the proposed method is applied to an exemplary set of extracted real-world data.

1 INTRODUCTION

Decreasing sensor costs lead to an increasing

prevalence of sensor systems as well as an increasing

availability of related data. Coupled with

advancements in the context of technologies and

methods to handle Big Data, this development

provides the foundation to exploit individual as well

as systematic benefits in the course of associated

transformation processes (i.e. Digital

Transformation) – a paradigm, that can be observed

in various industry sectors such as manufacturing

(e.g., Zhong et al., 2017), construction (e.g., Otte,

Zhou, et al., 2020; Zhou et al., 2020), policy-making

(e.g., Otte, Fenollar Solvay, & Meisen, 2020; Otte,

Gannouni, & Meisen, 2020; Otte & Meisen, 2021), or

education (e.g., Kaplan & Haenlein, 2016).

One sector that is expected to benefit from the

above-mentioned paradigm is the rail transport sector

(cf. Deutsche Bahn AG, 2021). Rail transport is,

https://orcid.org/0000-0002-4227-8938

https://orcid.org/0000-0003-3751-0680

https://orcid.org/0000-0002-1545-1416

among others, characterized by comparatively high

transport capacities per transport unit. This

characteristic also lays the foundation for achieving

comparatively high production extents of transport

volume – i.e. of passenger km (pkm) with regard to

passenger transport and/or ton km (tkm) with regard

to freight transport.

Consequently, especially in regions with large-

scale rail networks (cf. length, density) for short and

long-distance transport, rail transport is a sector of

particularly pronounced economic relevance. In

Germany, for example, the produced rail transport

volume grew between 2014 and 2019 for both

passenger (approx. +10% from 91 bn pkm to >100,4

bn pkm) and freight transport (approx. +15% from

115 bn tkm to >132,8 bn tkm) (BMVI, 2020).

Since rail transport, by nature, is an integrated

transport mode (cf. freight and passengers in the same

transport network), benefits that are obtained for

single transport volume shares (e.g., freight), directly

432

Otte, T., Posada-Moreno, A., Hübenthal, F., Haßler, M., Bartels, H., Abdelrazeq, A. and Hees, F.

Condition Monitoring of Rail Infrastructure and Rolling Stock using Acceleration Sensor Data of on-Rail Freight Wagons.

DOI: 10.5220/0010824600003122

In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 432-439

ISBN: 978-989-758-549-4; ISSN: 2184-4313

benefit not only the overall system but also the further

transport volume shares (e.g., passengers). Following

this overarching hypothesis, one key objective of the

project QUISS is to develop data-based applications

using modern data science approaches to detect

possible failures (e.g., of the rail infrastructure or

freight wagons) at an early stage by detecting patterns

and anomalies in data (BMVI, 2021). As a result of

subsequently reduced operational disruptions, the rail

transport system as a whole will benefit from this

through an increased service quality (e.g.,

punctuality) for customers (BMVI, 2021).

QUISS is carried out in a collaboration between

research and industry. One contributing industry

partner – among others – is the DB Cargo AG (i.e. the

business unit for rail freight transport of the Deutsche

Bahn AG), a company that operates a fleet of approx.

90,000 freight wagons and 3,400 locomotives (DB

Cargo AG, 2019). In the current roll-out stage, 65,000

freight wagons are going to be equipped with

telemetry-units containing multiple sensors such as

triaxial accelerometers – to date, approx. 94% (i.e.

approx. 61,000) of this sub-set of the overall freight

wagon fleet is already equipped.

The availability of related sensor data paves the

way for the use case ‘acceleration-based

infrastructure monitoring’, which enables the

monitoring of both rail infrastructure and rolling

stock without impairing the day-to-day business. For

additional information about flanking use cases

within the project QUISS, see (Otte, Bartels, et al.,

2020) and (Posada Moreno et al., 2020; Posada

Moreno et al., 2022)). In this paper, we present a

method to obtain insights and information advantages

from raw data (e.g., locations, acceleration data) that

was gathered from telemetry-units mounted on a fleet

of on-rail freight wagons. Furthermore, we apply the

proposed method on an exemplary set of real-world

freight wagon movement data and end with a

conclusion on related benefits for multiple involved

stakeholders (e.g., infrastructure providers, wagon

fleet operators).

2 RELATED WORK

Already in the early 1970s, research has been

conducted and presented concerning the relationship

of freight wagon movement and related technical

issues (e.g., vibration, shocks) (Roggeveen, 1972;

Scales, 1971; Simmons & Shackson, 1971). Since

then, advancements in the field of information and

communications technology (e.g., sensor systems,

broadband communication, availability of

computational resources) have enabled to conduct

further computer-aided analyses in order to gain

further insights into the analyzed aspects.

To obtain an overview of the existing related

work in this specific research domain, we conducted

a targeted literature analysis by applying pre-defined

keyword set combinations (cf. Table 1) for an

advanced search in Web of Science Core Collection

(Web of Science Group, 2020) (date: 2

March; time-

span: all-time; data field: ‘title’ OR ‘keywords’). At

this point, it should be highlighted that a different

scope of the literature collection (e.g., further

databases, further keywords) might lead to additional

candidates for the related work analysis.

The keyword set combinations represent

permutations of the following three fundamental

keyword sets:

KW Set I (Context): TI=((rail* OR wagon* OR

train*) AND (freight* OR cargo*)) OR AK=((rail*

OR wagon* OR train*) AND (freight* OR cargo*))

KW Set II (Digitalization): TI=(digit* OR

telem* OR internet* OR IoT* OR data*) OR

AK=(digit* OR telem* OR internet* OR IoT* OR

data*)

KW Set III (Use Case): TI=(shock* OR impact*

OR vibr* OR accel*) OR AK=(shock* OR impact*

OR vibr* OR accel*)

Table 1: Applied KW set combinations.

# Description n (collected)*

IV I AND II 68

V I AND III 105

VI I AND II AND III 2

* For further information on the collected initial corpus,

please contact the corresponding author.

In the next step, we selected the related works by

analyzing the titles and, if needed, the abstracts and/or

the full-text until the collected information were

sufficient for the decision whether to consider the

analyzed paper as related to our work.

The overall corpus of papers contains

contributions to various sub-domains of rail freight

transport (RFT). Among others, these are the design

and/or evaluation (e.g., profitability; energy

efficiency; safety) of the transport system (e.g.,

emissions; resilience), rail network (e.g., multi-modal

interaction), rolling stock (e.g., arrival time

predictions) or single components (e.g., sensor and

control systems) as well as inter-component

interactions (e.g., brake-wheel; wheel-rail).

To select contributions that are directly related to

our work, we derived the following exclusion criteria:

(1) objective (e.g., financial impact assessment); (2)

Condition Monitoring of Rail Infrastructure and Rolling Stock using Acceleration Sensor Data of on-Rail Freight Wagons

433

scope (e.g., single component analysis or component-

component interaction analyses); (3) perspective

(e.g., maintenance or intervention strategies); (4)

approach (e.g., component development, modal

analyses, material flow analyses). In case more than

one exclusion criterion was eligible to be assigned,

we decided based on the most decisive criterion.

As a result of the above-described selection

process, the following works could be identified as

directly related to our work:

Table 2: Related work.

Reference Focus

Brezulianu et al., 2020

control parameters

monitoring (real-time) for

freight fleets

Ußler et al., 2019

multi-sensor system

telematics platform for

freight wagons

Behrends et al., 2016;

Galonske et al., 2016

telematics-based information

services in RFT

Reason et al., 2010

sensor-enabled telemetry

within freight trains

Chiocchio et al., 2016

cloud-based platform for

freight train fleet

management

Aimar & Soma, 2017

sensor-enabled condition

monitoring of freight wagons

Macucci et al., 2015

sensor-enabled derailment

detection within freight trains

As one central outcome of the conducted

literature analysis, we can conclude that the suggested

methodological approach embodies a novel

contribution to the considered research domain. By

combining the suggested method with a first-time

application on real-world data, we emphasize the

feasibility of the approach and enable both

researchers as well as practitioners to reflect on the

transferability and expandability of our approach to

further applications (e.g., other economic sectors).

3 DATA SET AND METHOD

3.1 Overview: Data

The telemetry-units (battery-powered) are mounted

on freight wagons and collect data from incorporated

sensor modules (e.g., acceleration sensors, GPS

modules) as well as fuse them with existing meta data

(e.g., sensor system provider, wagon and train

identification numbers).

Among others, the sensor systems collect

geospatial temporal data (e.g., latitude, longitude,

instantaneous velocity) as well as additional data

about pre-defined events and send the collected data

to a data lake in a periodic fashion (i.e. every ten

minutes when moving, otherwise every 24 hours) and

in an event-triggered fashion.

From the perspective of the considered use case,

the events of interest are ‘shocks’. Shock events are

classified by processing measurements of the

available acceleration sensors in all spatial directions.

As soon as a configurable threshold value for one of

the three directions is exceeded, the acceleration data

is recorded and the according event is classified as a

shock event. Moreover, the data entry is enriched

with a corresponding position measurement and

timestamp.

The results obtained in this paper are based on an

extract of the entire data set provided by the

company-internal splunk-system (cf. Splunk, 2021)

and comprises the movement of 60 different wagons

over six months (May to October 2020). The wagons

were selected by calculating the average daily shock

rates for the first month (May 2020) and clustering

the results into pre-defined percentage ranges

spanning over the prevalent range of daily shock

rates. Finally, an equal number of wagons has been

selected from each percentage range with the

boundary condition that they moved on as many days

as possible.

The period contains 292,586 entries in total. The

heatmap visualization in Figure 1 illustrates the

geographic extent of the extracted data set.

Figure 1: Geographic extent of extracted data set.

3.2 Overview: Method

The utilized data analytics pipeline consists of six

steps starting with two pre-processing steps and

continuing with three processing steps as well as one

post-processing step:

ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods

434

(1) Read-In;

(2) Filtering;

(3) Shock Analysis;

(4) Anomaly Detection;

(5) Cluster Detection;

(6) Reverse Geocoding.

If desired, additional steps can be integrated into

the pipeline to gain further insights from the analyzed

data set.

4 RESULTS

Building upon the description of the dataset as well as

the proposed method, we subsequently describe and

execute the afore-mentioned sequence of single

process steps (cf. sub-sections 4.1 to 4.6).

4.1 Step 1: Read-in

In the context of the presented proof of concept, the

raw data files (e.g., daily or monthly extracts) are

obtained from the splunk system in the form of

‘comma-separated values’-files (*.csv) and merged

into one data frame (cf. Pandas, 2021) before being

saved accordingly for further processing.

4.2 Step 2: Filtering

The filtering process contains two sub-steps: position

and velocity filtering. First, the position filtering

removes data points with duplicate entries and

missing (e.g., due to lost GPS signal) or default

position information (e.g., due to set up and initial

operation process of the telemetry-unit).

Second, approximate mean velocities are

calculated by applying an Euler backward difference

scheme on the position information and the

corresponding timestamps within the remaining data

points. The geospatial difference is approximated by

the Haversine formula using the latitude and

longitude coordinates and the average radius of the

earth.

Subsequently, to ensure the physical plausibility

of prevalent velocities, calculated or measured

velocities greater than a pre-defined threshold were

filtered. Obtained from discussions with involved

domain experts, this threshold was set to 125 km/h.

As a positive side effect of this filtering process,

ensuring physically plausible velocities actively

reduces the probability to cause false positives in the

anomaly detection step (cf. sub-section 4.4). Table 3

summarizes the numerical effect of the filtering

process on the analyzed data set.

Table 3: Filtering overview.

Characteristic Value

Number of data points before filtering 292,586

Number of data points after filtering 275,207

Share of filtered data points 5.94%

Figure 2 illustrates the distribution of measured

and calculated velocities. The mean of the calculated

distribution is smaller than the mean of the measured

one, whereas the overall shape and tendency of both

distributions seem in good agreement. The

underestimation tendency for the calculated

velocities in comparison with the measured ones can

be attributed to the calculation method, as the driven

path length is estimated as the direct great circle

connection between two successive geospatial data

points on a sphere, thus ignoring the actual railway

course.

Figure 2: Distribution of measured and calculated

velocities.

4.3 Step 3: Shock Analysis

The shock analysis is divided into two parts: the full

time span shock analysis and the daily shock analysis.

4.3.1 Full Time Span

The full time span shock analysis iterates over each

wagon and over the entire time span of the data set

and calculates various shock-related measures. Table

4 and Table 5 provide an overview of the suggested

absolute and relative measures.

Table 4: Full time span shock measures (absolute).

Measure Description

max_num_shocks_per_train

Maximal number of shocks

detected for a train pulling

the wagon until train is

changed.

num_events

Total number of events for

the wagon over the entire

time span.

Condition Monitoring of Rail Infrastructure and Rolling Stock using Acceleration Sensor Data of on-Rail Freight Wagons

435

Table 4: Full time span shock measures (absolute) (cont.).

Measure Descri

tion

num_shocks

Total number of shocks

resp. shock events for the

wagon over the entire time

span.

num_trains

Total number of trains used

for pulling the wagon. If a

train is exchanged in the

past and the same train is

used at a later point in time,

this train counts twice.

Therefore, each train

change counts.

num_unique_trains

Total number of unique

trains used for pulling the

wagon. The same train

pulling the wagon at a later

point of time does not count

multi

le times.

num_trains_shock

Total number of trains with

at least one shock. Every

single train change counts

(cf. num_trains) and it is

checked for shocks until the

next train chan

e ha

ens.

num_unique_shock_trains

Total number of unique

trains used for pulling the

wagon with at least one

shock event.

num_days_driven

Total number of days the

wagon has been pulled by a

train.

num_shock_days

Total number of days the

wagon has been pulled by a

train and at least one shock

has been etected on the

corresponding day.

Table 5: Full time span shock measures (relative).

Measure Calculation

shock_rate_per_event

num_shocks /

num_events

shock_rate_per_train

num_trains_shock /

num_trains

shock_rate_per_day

num_shock_days /

num

driven

day_driven_per_total_days

num_days_driven /

num_days_df

4.3.2 Daily

The daily shock analysis iterates over each wagon and

over each day of the entire time span of the data set

and calculates the number of data points per day, the

number of shocks per day, and the number of trains

pulling the wagon (cf. Table 6). Based on that, the

daily shock rate for each wagon is determined as the

quotient of the total number of shocks and the total

number of entries on the corresponding day.

Table 6: Daily shock measures (absolute).

Measure Descri

tion

num

entries Total number of entries on this da

num_trains Total number of different trains of the

wagon on this day.

num_shocks Total number of shocks on this day.

4.3.3 Full Time Span vs. Daily

The full time span shock analysis offers an overview

of the entire data set, while in contrast to that, the

daily shock analysis provides time-dependent

measures, which in turn can be used in further

pipeline steps, e.g., detecting anomalies in the

analyzed data set.

4.4 Step 4: Anomaly Detection

As Figure 3 illustrates, our definition of an anomaly

is motivated by the hypothesis that level shifts within

the temporal progression of the daily shock rate can

be attributed to events that directly have an impact on

the condition of the rail infrastructure or the rolling

stock (e.g., maintenance events, sub-optimal railway

conditions, improper wagon handling). Contrarily,

non-anomalous wear and tear (e.g., of wagon material

or the rail infrastructure) is assumed to lead to a

comparatively gradual and continuous daily shock

rate growth.

Figure 3: Schematic sketch of hypothesis for anomaly

detection.

To detect level shift anomalies in the daily shock

rates, the LevelShiftAD anomaly detection algorithm

(taken from the Python ADTK package - see

ARUNDO ADTK, 2020a) is applied. The algorithm

was selected because it is not sensitive to

instantaneous spikes and suitable for frequently

occurring noisy outliers. The selected parameter

settings are documented in Table 7. For additional

information on the algorithm and parameters, see

(ARUNDO ADTK, 2020b).

ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods

436

When an anomalous day for a wagon is detected,

all data points of the corresponding day and wagon

are classified as anomalous.

Table 7: LevelShiftAD parameters.

Paramete

Value

c 6

window 5

side ‘both’

4.5 Step 5: Cluster Detection

After performing the anomaly detection, a clustering

analysis is carried out – optionally of the shock events

or the anomalous events. For the clustering analysis,

the scikit-learn implementation of the DBSCAN (i.e.

Density-Based Spatial Clustering of Applications

with Noise) algorithm is utilized. The parameters are

selected in a way that the cluster size is in the order

of the size of the detected shock cluster (e.g.,

industrial plant, railway station).

The selected settings for all non-default

DBSCAN parameters are summarized in Table 8. For

additional information on the algorithm and

parameters, see (scikit.learn, 2021).

Table 8: DBSCAN parameters.

Paramete

Value

eps_in_km 1 (eps_in_km / radius_earth)

min_samples 10

orithm ‘ball

tree’

metric ‘haversine’

An exemplary result for the described anomaly

detection followed by a subsequent cluster detection

and analysis after their application on the analyzed

real-world dataset is presented in Figure 4.

The shock cluster ranking is based on the full

dataset (i.e. 60 wagons), whereas the highlighted

anomaly detection results are presented for two

selected wagons that both traveled through the first-

ranked shock cluster. Some data points of one of the

wagons are classified as anomalous, while the data

points of the other wagon are completely anomalous-

free. Due to data privacy reasons, the depiction does

not contain underlying map information.

Remarkably, most of the anomalous data points

belong to the connection between the first and

second-ranked shock clusters, in which the wagon is

traveling multiple times during the entire time span of

the data set (six months). Furthermore, three anomaly

data points are detected west of the main anomalous

route, which are most probably caused by a singular

route change between clusters one and two.

The shock clusters #1 and #2 can be

geographically mapped to industrial plants, while

clusters #9 and #10 can be assigned to one railway

station and one marshaling yard respectively.

Figure 5 depicts a graphical representation of the

real-world data from the wagon that moved between

cluster #1 and #2 (cf. Figure 4). The figure does not

only depict the temporal progression of the daily

shock rate throughout the overall time span of the

analyzed data set but also highlights the absolute

number of pulling trains to which the wagon had been

assigned to. Furthermore, it enables the immediate

deduction of two further information: first, the share

of missing days (e.g., due to wagon idling or missing

signal transmission), and second, the extent of

anomalous days. The latter is of particular importance

for fleet operators as this information indicates

promising focal points for further in-depth analysis

(e.g., traveled routes, involved business users).

Figure 4: Map plot of two exemplary freight wagons.

Figure 5: Wagon-specific data representation.

4.6 Step 6: Reverse Geocoding

For the presented example, the pipeline is completed

by a ‘reverse geocoding’-step to map the latitude and

longitude data to the corresponding country code,

administrative region (cf. state, sub-state), and the

closest city. For this final and offline reverse

geocoding process, we utilized the Python package

called ‘reverse_geocoder’.

As the analyzed real-world data set was not

homogenous with regard to the share of data points per

country traveled, Table 9 represents an extract from the

overall statistical analysis to avoid highly misleading

conclusions (e.g., inter-country comparisons).

Condition Monitoring of Rail Infrastructure and Rolling Stock using Acceleration Sensor Data of on-Rail Freight Wagons

437

Table 9: Extract from statistical analysis.

Countr

% of Data Set Shocks Anomalies

DE 72.12 % 5.89% 4.09%

Building upon this initial analysis, further insights

can be obtained through multiple paths such as an in-

depth statistical analysis (e.g., shock rates per

country, anomaly rates per country). These insights

can be utilized as input parameters for future needs

for action – e.g., in the form of rankings and resulting

priorities for regions. Furthermore, subsequent

analysis steps can be implemented based on prevalent

individual requirements (e.g., objective of analysis;

configuration of data set).

5 CONCLUSION AND OUTLOOK

In the present paper, we showed how raw data from

multiple sensor systems mounted on freight wagons

can be used to monitor the condition of the prevalent

rail infrastructure as well as the rolling stock.

Consequently, equipping a fleet of freight (and/or

passenger) wagons with according telemetry-units

extends it to a moving sensor network that provides

not only momentary or short-term but also long-term

information about the transport system itself.

It has to be emphasized that the obtained results

are not to be understood as direct causalities (e.g.,

level shift in daily shock rate  damaged

infrastructure or improper handling of material).

Instead, each of the data-based findings serves as an

indication and a starting point from which to carry out

further in-depth data analyses or to apply additional

investigation methods (e.g., Alippi et al., 2000).

When interpreting the results obtained, the integration

of human expert knowledge of the application domain

is essential, just as it is when guiding through and

performing subsequent in-depth analyses.

Based on our present paper, future work should

address the following aspects in particular: a

systematic in-depth analysis of individual wagon

numbers and clusters of similar wagons; enrichment

with additional data sources (e.g., maintenance

plans), implementation of further steps to the

analytics pipeline and transnational stakeholder

exchange (e.g., infrastructure providers, fleet

operators).

Considering the context of large (freight) wagon

fleets (cf. multiple millions of data points per calendar

week), special attention should be paid to the

automation (e.g., computing frequency), scalability

(e.g., parallel computing), and efficiency (e.g.,

resource-efficient programming) of the

computational operations to pave the way towards an

industrial usage of the suggested method.

ACKNOWLEDGEMENTS

The authors acknowledge the financial support by the

Federal Ministry of Transport and Digital

Infrastructure of Germany (BMVI) within the project

QUISS (project number 19F2060).

The authors further acknowledge the support of

Christoph Anger and Daniel Wolfram (both DB

Cargo AG) in the course of the data exchange and the

related fruitful discussions.

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