---
_id: '27652'
abstract:
- lang: ger
text: "Aufgrund der Fortschritte der Digitalisierung finden Systeme zur Zustandsüberwachung
vermehrt Einsatz in der Industrie, um durch eine zustandsbasierte oder eine prädiktive
Instandhaltung Vorteile, wie eine verbesserte Zuverlässigkeit und geringere Kosten
zu erzielen. Dabei beruhen Zustandsüberwachungssysteme auf den folgenden Bausteinen:
Sensorik, Datenvorverarbeitung, Merkmalsextraktion und -auswahl, Diagnose bzw.
Prognose sowie einer Entscheidungsfindung basierend auf den Ergebnissen. Jeder
dieser Bausteine erfordert individuelle Einstellungen, um ein geeignetes Zustandsüberwachungssystem
für die jeweilige Anwendung zu entwickeln. Eine offene Fragestellung im Bereich
der Zustandsüberwachung ergibt sich aufgrund der Unsicherheit der Zukunft, die
sich in den zukünftigen Betriebs- und Umgebungsbedingungen zeigt. Diese Unsicherheit
gilt es in allen Bausteinen zu berücksichtigen.\r\nDieser Beitrag konzentriert
sich auf den Baustein Merkmalsextraktion und -selektion, mit dem Ziel anhand geeigneter
Merkmale eine Prognose der nutzbaren Restlebensdauer mit hoher Genauigkeit realisieren
zu können. Daher werden geeignete Merkmale aus dem Zeitbereich und daraus abgeleitete
Zustandsindikatoren für die Restlebensdauerprognose von technischen Systemen vorgestellt.
Dabei sind Zustandsindikatoren Kenngrößen zur Beobachtung des Zustands der kritischen
Systemkomponenten. Anhand dreier Anwendungsbeispiele wird ihre Eignung evaluiert.
Dabei werden Daten aus Lebensdauerversuchen unter instationären Betriebs- und
Umgebungsbedingungen ausgewertet. Die auftretenden Unsicherheiten der Zukunft
werden somit berücksichtigt. Die Beispielsysteme beruhen auf Gummi-Metall-Elementen
und Wälzlagern. Aus den generierten Ergebnissen lässt sich schließen, dass die
Zustandsindikatoren aus der betrachteten Zeitreihen-Toolbox auch unter unbekannten
Betriebs- und Umgebungsbedingungen robust sind.\r\n"
- lang: eng
text: "Due to the advances in digitalization, condition monitoring systems have
found numerous applications in the industry due to benefits such as improved reliability
and lowered costs through condition-based or predictive maintenance. Condition
monitoring systems typically involve elements, such as data acquisition via suitable
sensors, data preprocessing, feature extraction and selection, diagnostics, prognostics
and (maintenance) decisions based on diagnosis or prognosis. For the application-specific
development of a suitable condition monitoring system, each of these elements
requires individual settings. Due to the uncertainty of the future, an open question
arises in the condition monitoring field, which is reflected in unknown future
operating and environmental conditions. This uncertainty needs consideration in
all elements of a condition monitoring system.\r\nThis article focuses on feature
extraction and selection, building on the hypothesis that the remaining useful
life of a technical system can be predicted with high accuracy utilizing suitable
features. In this article, health indicators derived from time-domain features
that permit the monitoring of the health of critical system components are presented
for predicting the remaining useful life of technical systems. Three distinct
application examples based on rubber-metal elements and rolling-element bearings
are evaluated to validate the suitability of the presented methods. Experimental
data from accelerated lifetime tests conducted under non-stationary operating
and environmental conditions are considered to take possible future uncertainties
into account. It can be concluded from the acquired results that health indicators
derived from the presented time series toolbox are robust to varying operating
and environmental conditions.\r\n"
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Bender A, Sextro W. Extraktion und Selektion geeigneter Merkmale
für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten
. In: VDI-Berichte 2391. VDI Verlag GmbH; 2021:197-210.'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2021). Extraktion und Selektion
geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz
aleatorischen Unsicherheiten . VDI-Berichte 2391, 197–210.
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2021, place={Düsseldorf}, title={Extraktion
und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen
Systemen trotz aleatorischen Unsicherheiten }, booktitle={VDI-Berichte 2391},
publisher={VDI Verlag GmbH}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
Amelie and Sextro, Walter}, year={2021}, pages={197–210} }'
chicago: 'Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Extraktion
und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen
Systemen trotz aleatorischen Unsicherheiten .” In VDI-Berichte 2391, 197–210.
Düsseldorf: VDI Verlag GmbH, 2021.'
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Extraktion und Selektion geeigneter
Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen
Unsicherheiten ,” in VDI-Berichte 2391, Würzburg, 2021, pp. 197–210.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Extraktion und Selektion geeigneter
Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen
Unsicherheiten .” VDI-Berichte 2391, VDI Verlag GmbH, 2021, pp. 197–210.
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: VDI-Berichte 2391, VDI Verlag
GmbH, Düsseldorf, 2021, pp. 197–210.'
conference:
end_date: 2021-11-17
location: Würzburg
name: '3. VDI-Fachtagung '
start_date: 2021-11-16
date_created: 2021-11-22T07:42:44Z
date_updated: 2022-01-06T06:57:43Z
department:
- _id: '151'
keyword:
- run-to-failure
- rubber-metal element
- bearing prognostics
- non-stationary operating conditions
- varying operating conditions
- feature extraction
- feature selection
language:
- iso: ger
page: 197 - 210
place: Düsseldorf
publication: VDI-Berichte 2391
publication_identifier:
isbn:
- 978-3-18-092391-8
issn:
- '0083-5560 '
publication_status: published
publisher: VDI Verlag GmbH
status: public
title: 'Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose
von technischen Systemen trotz aleatorischen Unsicherheiten '
type: conference
user_id: '9557'
year: '2021'
...
---
_id: '22507'
abstract:
- lang: eng
text: Several methods, including order analysis, wavelet analysis and empirical
mode decomposition have been proposed and successfully employed for the health
state estimation of technical systems operating under varying conditions. However,
where information such as the speed of rotating machinery, component specifications
or other domain-specific information is unavailable, such methods are often infeasible.
Thus, this paper investigates the application of classical time-domain features,
features from the medical field and novel features from the highly comparative
time-series analysis (HCTSA) package, for the health state estimation of rotating
machinery operating under varying conditions. Furthermore, several feature selection
methods are investigated to identify features as viable health indicators for
the diagnostics and prognostics of technical systems. As a case study, the presented
methods are evaluated on real-world and experimentally acquired vibration data
of bearings operating under varying speed. The results show that the selected
features can successfully be employed as health indicators for technical systems
operating under varying conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Bender A, Sextro W. On the applicability of time series features
as health indicators for technical systems operating under varying conditions.
In: Proceedings of the Seventeenth International Conference on Condition Monitoring
and Asset Management (CM 2021).'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (n.d.). On the applicability
of time series features as health indicators for technical systems operating under
varying conditions. Proceedings of the Seventeenth International Conference
on Condition Monitoring and Asset Management (CM 2021). Seventeenth International
Conference on Condition Monitoring and Asset Management (CM 2021).
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro, title={On the applicability
of time series features as health indicators for technical systems operating under
varying conditions}, booktitle={Proceedings of the Seventeenth International Conference
on Condition Monitoring and Asset Management (CM 2021)}, author={Aimiyekagbon,
Osarenren Kennedy and Bender, Amelie and Sextro, Walter} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “On
the Applicability of Time Series Features as Health Indicators for Technical Systems
Operating under Varying Conditions.” In Proceedings of the Seventeenth International
Conference on Condition Monitoring and Asset Management (CM 2021), n.d.
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “On the applicability of time
series features as health indicators for technical systems operating under varying
conditions,” presented at the Seventeenth International Conference on Condition
Monitoring and Asset Management (CM 2021).
mla: Aimiyekagbon, Osarenren Kennedy, et al. “On the Applicability of Time Series
Features as Health Indicators for Technical Systems Operating under Varying Conditions.”
Proceedings of the Seventeenth International Conference on Condition Monitoring
and Asset Management (CM 2021).
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: Proceedings of the Seventeenth
International Conference on Condition Monitoring and Asset Management (CM 2021),
n.d.'
conference:
end_date: 2021-06-18
name: Seventeenth International Conference on Condition Monitoring and Asset Management
(CM 2021)
start_date: 2021-06-14
date_created: 2021-06-23T05:24:39Z
date_updated: 2023-09-22T08:10:34Z
ddc:
- '620'
department:
- _id: '151'
file:
- access_level: open_access
content_type: application/pdf
creator: kennedy
date_created: 2021-06-23T06:43:44Z
date_updated: 2021-06-23T06:50:07Z
description: 'This is a post-print version of the article presented at the Seventeenth
International Con-ference on Condition Monitoring and Asset Management (CM 2021).
The event websiteis available at: https://www.bindt.org/events/CM-2021/ and the
abstract is available at:https://www.bindt.org/events/CM-2021/abstract-9a7/.'
file_id: '22508'
file_name: Aimiyekagbon_et_al_2021_On_the_applicability_of_time_series_features_as_health_indicators_postPrint.pdf
file_size: 1875572
relation: main_file
title: On the applicability of time series features as health indicators for technical
systems operating under varying conditions
file_date_updated: 2021-06-23T06:50:07Z
has_accepted_license: '1'
keyword:
- Wind turbine diagnostics
- bearing diagnostics
- non-stationary operating conditions
- varying operating conditions
- feature extraction
- feature selection
- fault detection
- failure detection
language:
- iso: eng
oa: '1'
publication: Proceedings of the Seventeenth International Conference on Condition
Monitoring and Asset Management (CM 2021)
publication_status: inpress
quality_controlled: '1'
status: public
title: On the applicability of time series features as health indicators for technical
systems operating under varying conditions
type: conference
user_id: '9557'
year: '2021'
...
---
_id: '27111'
abstract:
- lang: eng
text: In the industry 4.0 era, there is a growing need to transform unstructured
data acquired by a multitude of sources into information and subsequently into
knowledge to improve the quality of manufactured products, to boost production,
for predictive maintenance, etc. Data-driven approaches, such as machine learning
techniques, are typically employed to model the underlying relationship from data.
However, an increase in model accuracy with state-of-the-art methods, such as
deep convolutional neural networks, results in less interpretability and transparency.
Due to the ease of implementation, interpretation and transparency to both domain
experts and non-experts, a rule-based method is proposed in this paper, for prognostics
and health management (PHM) and specifically for diagnostics. The proposed method
utilizes the most relevant sensor signals acquired via feature extraction and
selection techniques and expert knowledge. As a case study, the presented method
is evaluated on data from a real-world quality control set-up provided by the
European prognostics and health management society (PHME) at the conference’s
2021 data challenge. With the proposed method, our team took the third place,
capable of successfully diagnosing different fault modes, irrespective of varying
conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
of a Production Line. In: Do P, King S, Fink O, eds. Proceedings of the European
Conference of the PHM Society 2021. Vol 6. ; 2021:527-536. doi:10.36001/phme.2021.v6i1.3042'
apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro,
W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &
O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042
bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
Diagnostics of a Production Line}, volume={6}, DOI={10.36001/phme.2021.v6i1.3042},
number={1}, booktitle={Proceedings of the European Conference of the PHM Society
2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
and Fink, Olga}, year={2021}, pages={527–536} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In Proceedings
of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve
King, and Olga Fink, 6:527–36, 2021. https://doi.org/10.36001/phme.2021.v6i1.3042.
ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
Diagnostics of a Production Line,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, pp. 527–536, doi: 10.36001/phme.2021.v6i1.3042.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
Line.” Proceedings of the European Conference of the PHM Society 2021,
edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042.
short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
Society 2021, 2021, pp. 527–536.'
conference:
name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
full_name: Do, Phuc
last_name: Do
- first_name: Steve
full_name: King, Steve
last_name: King
- first_name: Olga
full_name: Fink, Olga
last_name: Fink
intvolume: ' 6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...
---
_id: '48873'
abstract:
- lang: eng
text: Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP)
heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful
in generating satisfactory or even optimal solutions. However, the reasons for
their success are not yet fully understood. Recent approaches take an analytical
viewpoint and try to identify instance features, which make an instance hard or
easy to solve. We contribute to this area by generating instance sets for couples
of TSP algorithms A and B by maximizing/minimizing their performance difference
in order to generate instances which are easier to solve for one solver and much
harder to solve for the other. This instance set offers the potential to identify
key features which allow to distinguish between the problem hardness classes of
both algorithms.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Trautmann H. Evolving Instances for Maximizing Performance Differences
of State-of-the-Art Inexact TSP Solvers. In: Festa P, Sellmann M, Vanschoren J,
eds. Learning and Intelligent Optimization. Lecture Notes in Computer Science.
Springer International Publishing; 2016:48–59. doi:10.1007/978-3-319-50349-3_4'
apa: Bossek, J., & Trautmann, H. (2016). Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers. In P. Festa, M. Sellmann,
& J. Vanschoren (Eds.), Learning and Intelligent Optimization (pp.
48–59). Springer International Publishing. https://doi.org/10.1007/978-3-319-50349-3_4
bibtex: '@inproceedings{Bossek_Trautmann_2016, place={Cham}, series={Lecture Notes
in Computer Science}, title={Evolving Instances for Maximizing Performance Differences
of State-of-the-Art Inexact TSP Solvers}, DOI={10.1007/978-3-319-50349-3_4},
booktitle={Learning and Intelligent Optimization}, publisher={Springer International
Publishing}, author={Bossek, Jakob and Trautmann, Heike}, editor={Festa, Paola
and Sellmann, Meinolf and Vanschoren, Joaquin}, year={2016}, pages={48–59}, collection={Lecture
Notes in Computer Science} }'
chicago: 'Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing
Performance Differences of State-of-the-Art Inexact TSP Solvers.” In Learning
and Intelligent Optimization, edited by Paola Festa, Meinolf Sellmann, and
Joaquin Vanschoren, 48–59. Lecture Notes in Computer Science. Cham: Springer International
Publishing, 2016. https://doi.org/10.1007/978-3-319-50349-3_4.'
ieee: 'J. Bossek and H. Trautmann, “Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers,” in Learning and Intelligent
Optimization, 2016, pp. 48–59, doi: 10.1007/978-3-319-50349-3_4.'
mla: Bossek, Jakob, and Heike Trautmann. “Evolving Instances for Maximizing Performance
Differences of State-of-the-Art Inexact TSP Solvers.” Learning and Intelligent
Optimization, edited by Paola Festa et al., Springer International Publishing,
2016, pp. 48–59, doi:10.1007/978-3-319-50349-3_4.
short: 'J. Bossek, H. Trautmann, in: P. Festa, M. Sellmann, J. Vanschoren (Eds.),
Learning and Intelligent Optimization, Springer International Publishing, Cham,
2016, pp. 48–59.'
date_created: 2023-11-14T15:58:57Z
date_updated: 2023-12-13T10:47:05Z
department:
- _id: '819'
doi: 10.1007/978-3-319-50349-3_4
editor:
- first_name: Paola
full_name: Festa, Paola
last_name: Festa
- first_name: Meinolf
full_name: Sellmann, Meinolf
last_name: Sellmann
- first_name: Joaquin
full_name: Vanschoren, Joaquin
last_name: Vanschoren
extern: '1'
keyword:
- Algorithm selection
- Feature selection
- Instance hardness
- TSP
language:
- iso: eng
page: 48–59
place: Cham
publication: Learning and Intelligent Optimization
publication_identifier:
isbn:
- 978-3-319-50349-3
publication_status: published
publisher: Springer International Publishing
series_title: Lecture Notes in Computer Science
status: public
title: Evolving Instances for Maximizing Performance Differences of State-of-the-Art
Inexact TSP Solvers
type: conference
user_id: '102979'
year: '2016'
...
---
_id: '9880'
abstract:
- lang: eng
text: With the paradigm shift towards prognostic and health management (PHM) of
machinery, there is need for reliable PHM methodologies with narrow error bounds
to allow maintenance engineers take decisive maintenance actions based on the
prognostic results. Prognostics is mainly concerned with the estimation of the
remaining useful life (RUL) or time to failure (TTF). The accuracy of PHM methods
is usually a function of the features extracted from the raw data obtained from
sensors. In cases where the extracted features do not display clear degradation
trends, for instance highly loaded bearings, the accuracy of the state of the
art PHM methods is significantly affected. The data which lacks clear degradation
trend is referred to as non-trending data. This study presents a method for extracting
degradation trends from non-trending condition monitoring data for RUL estimation.
The raw signals are first filtered using a discrete wavelet transform (DWT) denoising
filter to remove noise from the acquired signals. Time domain, frequency domain
and time-frequency domain features are then extracted from the filtered signals.
An autoregressive model is then applied to the extracted features to identify
the degradation trends. Features representing the maximum health information are
then selected based on a performance evaluation criteria using extreme learning
machine (ELM) algorithm. The selected features can then be used as inputs in a
prognostic algorithm. The feasibility of the method is demonstrated using experimental
bearing vibration data. The performance of the method is evaluated on the accuracy
of RUL estimation and the results show that the method can be used to accurately
estimate RUL with a maximum error of 10\%.
author:
- first_name: James Kuria
full_name: Kimotho, James Kuria
last_name: Kimotho
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Sextro W. An approach for feature extraction and selection from
non-trending data for machinery prognosis. In: Proceedings of the Second European
Conference of the Prognostics and Health Management Society 2014. Vol 5. ;
2014.'
apa: Kimotho, J. K., & Sextro, W. (2014). An approach for feature extraction
and selection from non-trending data for machinery prognosis. In Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014 (Vol. 5).
bibtex: '@inproceedings{Kimotho_Sextro_2014, title={An approach for feature extraction
and selection from non-trending data for machinery prognosis}, volume={5}, booktitle={Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014}, author={Kimotho, James Kuria and Sextro, Walter}, year={2014} }'
chicago: Kimotho, James Kuria, and Walter Sextro. “An Approach for Feature Extraction
and Selection from Non-Trending Data for Machinery Prognosis.” In Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014, Vol. 5, 2014.
ieee: J. K. Kimotho and W. Sextro, “An approach for feature extraction and selection
from non-trending data for machinery prognosis,” in Proceedings of the Second
European Conference of the Prognostics and Health Management Society 2014,
2014, vol. 5.
mla: Kimotho, James Kuria, and Walter Sextro. “An Approach for Feature Extraction
and Selection from Non-Trending Data for Machinery Prognosis.” Proceedings
of the Second European Conference of the Prognostics and Health Management Society
2014, vol. 5, 2014.
short: 'J.K. Kimotho, W. Sextro, in: Proceedings of the Second European Conference
of the Prognostics and Health Management Society 2014, 2014.'
date_created: 2019-05-20T13:13:00Z
date_updated: 2019-09-16T10:37:35Z
department:
- _id: '151'
intvolume: ' 5'
keyword:
- autoregressive model ELM feature extraction feature selection non-trending Remaining
useful Life
language:
- iso: eng
publication: Proceedings of the Second European Conference of the Prognostics and
Health Management Society 2014
quality_controlled: '1'
status: public
title: An approach for feature extraction and selection from non-trending data for
machinery prognosis
type: conference
user_id: '55222'
volume: 5
year: '2014'
...
---
_id: '46388'
abstract:
- lang: eng
text: Understanding the behaviour of well-known algorithms for classical NP-hard
optimisation problems is still a difficult task. With this paper, we contribute
to this research direction and carry out a feature based comparison of local search
and the well-known Christofides approximation algorithm for the Traveling Salesperson
Problem. We use an evolutionary algorithm approach to construct easy and hard
instances for the Christofides algorithm, where we measure hardness in terms of
approximation ratio. Our results point out important features and lead to hard
and easy instances for this famous algorithm. Furthermore, our cross-comparison
gives new insights on the complementary benefits of the different approaches.
author:
- first_name: Samadhi
full_name: Nallaperuma, Samadhi
last_name: Nallaperuma
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'Nallaperuma S, Wagner M, Neumann F, Bischl B, Mersmann O, Trautmann H. A Feature-Based
Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson
Problem. In: Proceedings of the Twelfth Workshop on Foundations of Genetic
Algorithms XII. FOGA XII ’13. Association for Computing Machinery; 2013:147–160.
doi:10.1145/2460239.2460253'
apa: Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., & Trautmann,
H. (2013). A Feature-Based Comparison of Local Search and the Christofides Algorithm
for the Travelling Salesperson Problem. Proceedings of the Twelfth Workshop
on Foundations of Genetic Algorithms XII, 147–160. https://doi.org/10.1145/2460239.2460253
bibtex: '@inproceedings{Nallaperuma_Wagner_Neumann_Bischl_Mersmann_Trautmann_2013,
place={New York, NY, USA}, series={FOGA XII ’13}, title={A Feature-Based Comparison
of Local Search and the Christofides Algorithm for the Travelling Salesperson
Problem}, DOI={10.1145/2460239.2460253},
booktitle={Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms
XII}, publisher={Association for Computing Machinery}, author={Nallaperuma, Samadhi
and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and
Trautmann, Heike}, year={2013}, pages={147–160}, collection={FOGA XII ’13} }'
chicago: 'Nallaperuma, Samadhi, Markus Wagner, Frank Neumann, Bernd Bischl, Olaf
Mersmann, and Heike Trautmann. “A Feature-Based Comparison of Local Search and
the Christofides Algorithm for the Travelling Salesperson Problem.” In Proceedings
of the Twelfth Workshop on Foundations of Genetic Algorithms XII, 147–160.
FOGA XII ’13. New York, NY, USA: Association for Computing Machinery, 2013. https://doi.org/10.1145/2460239.2460253.'
ieee: 'S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and H. Trautmann,
“A Feature-Based Comparison of Local Search and the Christofides Algorithm for
the Travelling Salesperson Problem,” in Proceedings of the Twelfth Workshop
on Foundations of Genetic Algorithms XII, 2013, pp. 147–160, doi: 10.1145/2460239.2460253.'
mla: Nallaperuma, Samadhi, et al. “A Feature-Based Comparison of Local Search and
the Christofides Algorithm for the Travelling Salesperson Problem.” Proceedings
of the Twelfth Workshop on Foundations of Genetic Algorithms XII, Association
for Computing Machinery, 2013, pp. 147–160, doi:10.1145/2460239.2460253.
short: 'S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, H. Trautmann,
in: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII,
Association for Computing Machinery, New York, NY, USA, 2013, pp. 147–160.'
date_created: 2023-08-04T15:42:03Z
date_updated: 2023-10-16T13:45:53Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2460239.2460253
keyword:
- approximation algorithms
- local search
- traveling salesperson problem
- feature selection
- prediction
- classification
language:
- iso: eng
page: 147–160
place: New York, NY, USA
publication: Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms
XII
publication_identifier:
isbn:
- '9781450319904'
publisher: Association for Computing Machinery
series_title: FOGA XII ’13
status: public
title: A Feature-Based Comparison of Local Search and the Christofides Algorithm for
the Travelling Salesperson Problem
type: conference
user_id: '15504'
year: '2013'
...
---
_id: '48889'
abstract:
- lang: eng
text: Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization
problems. With this paper we contribute to the understanding of the success of
2-opt based local search algorithms for solving the traveling salesperson problem
(TSP). Although 2-opt is widely used in practice, it is hard to understand its
success from a theoretical perspective. We take a statistical approach and examine
the features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: Mersmann O, Bischl B, Trautmann H, Wagner M, Bossek J, Neumann F. A Novel Feature-Based
Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem.
Annals of Mathematics and Artificial Intelligence. 2013;69(2):151–182.
doi:10.1007/s10472-013-9341-2
apa: Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., & Neumann,
F. (2013). A Novel Feature-Based Approach to Characterize Algorithm Performance
for the Traveling Salesperson Problem. Annals of Mathematics and Artificial
Intelligence, 69(2), 151–182. https://doi.org/10.1007/s10472-013-9341-2
bibtex: '@article{Mersmann_Bischl_Trautmann_Wagner_Bossek_Neumann_2013, title={A
Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling
Salesperson Problem}, volume={69}, DOI={10.1007/s10472-013-9341-2},
number={2}, journal={Annals of Mathematics and Artificial Intelligence}, author={Mersmann,
Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob
and Neumann, Frank}, year={2013}, pages={151–182} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Markus Wagner, Jakob Bossek,
and Frank Neumann. “A Novel Feature-Based Approach to Characterize Algorithm Performance
for the Traveling Salesperson Problem.” Annals of Mathematics and Artificial
Intelligence 69, no. 2 (2013): 151–182. https://doi.org/10.1007/s10472-013-9341-2.'
ieee: 'O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann,
“A Novel Feature-Based Approach to Characterize Algorithm Performance for the
Traveling Salesperson Problem,” Annals of Mathematics and Artificial Intelligence,
vol. 69, no. 2, pp. 151–182, 2013, doi: 10.1007/s10472-013-9341-2.'
mla: Mersmann, Olaf, et al. “A Novel Feature-Based Approach to Characterize Algorithm
Performance for the Traveling Salesperson Problem.” Annals of Mathematics and
Artificial Intelligence, vol. 69, no. 2, 2013, pp. 151–182, doi:10.1007/s10472-013-9341-2.
short: O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, F. Neumann, Annals
of Mathematics and Artificial Intelligence 69 (2013) 151–182.
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:50:41Z
department:
- _id: '819'
doi: 10.1007/s10472-013-9341-2
intvolume: ' 69'
issue: '2'
keyword:
- 2-opt
- 90B06
- Classification
- Feature selection
- MARS
- TSP
language:
- iso: eng
page: 151–182
publication: Annals of Mathematics and Artificial Intelligence
publication_identifier:
issn:
- 1012-2443
status: public
title: A Novel Feature-Based Approach to Characterize Algorithm Performance for the
Traveling Salesperson Problem
type: journal_article
user_id: '102979'
volume: 69
year: '2013'
...
---
_id: '48890'
abstract:
- lang: eng
text: With this paper we contribute to the understanding of the success of 2-opt
based local search algorithms for solving the traveling salesman problem TSP.
Although 2-opt is widely used in practice, it is hard to understand its success
from a theoretical perspective. We take a statistical approach and examine the
features of TSP instances that make the problem either hard or easy to solve.
As a measure of problem difficulty for 2-opt we use the approximation ratio that
it achieves on a given instance. Our investigations point out important features
that make TSP instances hard or easy to be approximated by 2-opt.
author:
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
- first_name: Markus
full_name: Wagner, Markus
last_name: Wagner
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Mersmann O, Bischl B, Bossek J, Trautmann H, Wagner M, Neumann F. Local Search
and the Traveling Salesman Problem: A Feature-Based Characterization of Problem
Hardness. In: Revised Selected Papers of the 6th International Conference on
Learning and Intelligent Optimization - Volume 7219. LION 6. Springer-Verlag;
2012:115–129.'
apa: 'Mersmann, O., Bischl, B., Bossek, J., Trautmann, H., Wagner, M., & Neumann,
F. (2012). Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness. Revised Selected Papers of the 6th International Conference
on Learning and Intelligent Optimization - Volume 7219, 115–129.'
bibtex: '@inproceedings{Mersmann_Bischl_Bossek_Trautmann_Wagner_Neumann_2012, place={Berlin,
Heidelberg}, series={LION 6}, title={Local Search and the Traveling Salesman Problem:
A Feature-Based Characterization of Problem Hardness}, booktitle={Revised Selected
Papers of the 6th International Conference on Learning and Intelligent Optimization
- Volume 7219}, publisher={Springer-Verlag}, author={Mersmann, Olaf and Bischl,
Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank},
year={2012}, pages={115–129}, collection={LION 6} }'
chicago: 'Mersmann, Olaf, Bernd Bischl, Jakob Bossek, Heike Trautmann, Markus Wagner,
and Frank Neumann. “Local Search and the Traveling Salesman Problem: A Feature-Based
Characterization of Problem Hardness.” In Revised Selected Papers of the 6th
International Conference on Learning and Intelligent Optimization - Volume 7219,
115–129. LION 6. Berlin, Heidelberg: Springer-Verlag, 2012.'
ieee: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, and F. Neumann,
“Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness,” in Revised Selected Papers of the 6th International Conference
on Learning and Intelligent Optimization - Volume 7219, 2012, pp. 115–129.'
mla: 'Mersmann, Olaf, et al. “Local Search and the Traveling Salesman Problem: A
Feature-Based Characterization of Problem Hardness.” Revised Selected Papers
of the 6th International Conference on Learning and Intelligent Optimization -
Volume 7219, Springer-Verlag, 2012, pp. 115–129.'
short: 'O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, M. Wagner, F. Neumann,
in: Revised Selected Papers of the 6th International Conference on Learning and
Intelligent Optimization - Volume 7219, Springer-Verlag, Berlin, Heidelberg, 2012,
pp. 115–129.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:48:58Z
department:
- _id: '819'
extern: '1'
keyword:
- 2-opt
- Classification
- Feature Selection
- MARS
- TSP
language:
- iso: eng
page: 115–129
place: Berlin, Heidelberg
publication: Revised Selected Papers of the 6th International Conference on Learning
and Intelligent Optimization - Volume 7219
publication_identifier:
isbn:
- 978-3-642-34412-1
publisher: Springer-Verlag
series_title: LION 6
status: public
title: 'Local Search and the Traveling Salesman Problem: A Feature-Based Characterization
of Problem Hardness'
type: conference
user_id: '102979'
year: '2012'
...