@inproceedings{27652, abstract = {{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. Dieser 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. }}, author = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}}, booktitle = {{VDI-Berichte 2391}}, isbn = {{978-3-18-092391-8}}, issn = {{0083-5560 }}, keywords = {{run-to-failure, rubber-metal element, bearing prognostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection}}, location = {{Würzburg}}, pages = {{197 -- 210}}, publisher = {{VDI Verlag GmbH}}, title = {{{Extraktion und Selektion geeigneter Merkmale für die Restlebensdauerprognose von technischen Systemen trotz aleatorischen Unsicherheiten }}}, year = {{2021}}, } @inproceedings{22507, abstract = {{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 = {{Aimiyekagbon, Osarenren Kennedy and Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the Seventeenth International Conference on Condition Monitoring and Asset Management (CM 2021)}}, keywords = {{Wind turbine diagnostics, bearing diagnostics, non-stationary operating conditions, varying operating conditions, feature extraction, feature selection, fault detection, failure detection}}, title = {{{On the applicability of time series features as health indicators for technical systems operating under varying conditions}}}, year = {{2021}}, } @inproceedings{27111, abstract = {{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 = {{Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike Claudia and Bender, Amelie and Sextro, Walter}}, booktitle = {{Proceedings of the European Conference of the PHM Society 2021}}, editor = {{Do, Phuc and King, Steve and Fink, Olga}}, keywords = {{PHME 2021, Feature Selection Classification, Feature Selection Clustering, Interpretable Model, Transparent Model, Industry 4.0, Real-World Diagnostics, Quality Control, Predictive Maintenance}}, number = {{1}}, pages = {{527--536}}, title = {{{Rule-based Diagnostics of a Production Line}}}, doi = {{10.36001/phme.2021.v6i1.3042}}, volume = {{6}}, year = {{2021}}, } @inproceedings{48873, abstract = {{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 = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Festa, Paola and Sellmann, Meinolf and Vanschoren, Joaquin}}, isbn = {{978-3-319-50349-3}}, keywords = {{Algorithm selection, Feature selection, Instance hardness, TSP}}, pages = {{48–59}}, publisher = {{Springer International Publishing}}, title = {{{Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers}}}, doi = {{10.1007/978-3-319-50349-3_4}}, year = {{2016}}, } @inproceedings{9880, abstract = {{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 = {{Kimotho, James Kuria and Sextro, Walter}}, booktitle = {{Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014}}, keywords = {{autoregressive model ELM feature extraction feature selection non-trending Remaining useful Life}}, title = {{{An approach for feature extraction and selection from non-trending data for machinery prognosis}}}, volume = {{5}}, year = {{2014}}, } @inproceedings{46388, abstract = {{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 = {{Nallaperuma, Samadhi and Wagner, Markus and Neumann, Frank and Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike}}, booktitle = {{Proceedings of the Twelfth Workshop on Foundations of Genetic Algorithms XII}}, isbn = {{9781450319904}}, keywords = {{approximation algorithms, local search, traveling salesperson problem, feature selection, prediction, classification}}, pages = {{147–160}}, publisher = {{Association for Computing Machinery}}, title = {{{A Feature-Based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem}}}, doi = {{10.1145/2460239.2460253}}, year = {{2013}}, } @article{48889, abstract = {{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 = {{Mersmann, Olaf and Bischl, Bernd and Trautmann, Heike and Wagner, Markus and Bossek, Jakob and Neumann, Frank}}, issn = {{1012-2443}}, journal = {{Annals of Mathematics and Artificial Intelligence}}, keywords = {{2-opt, 90B06, Classification, Feature selection, MARS, TSP}}, number = {{2}}, pages = {{151–182}}, title = {{{A Novel Feature-Based Approach to Characterize Algorithm Performance for the Traveling Salesperson Problem}}}, doi = {{10.1007/s10472-013-9341-2}}, volume = {{69}}, year = {{2013}}, } @inproceedings{48890, abstract = {{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 = {{Mersmann, Olaf and Bischl, Bernd and Bossek, Jakob and Trautmann, Heike and Wagner, Markus and Neumann, Frank}}, booktitle = {{Revised Selected Papers of the 6th International Conference on Learning and Intelligent Optimization - Volume 7219}}, isbn = {{978-3-642-34412-1}}, keywords = {{2-opt, Classification, Feature Selection, MARS, TSP}}, pages = {{115–129}}, publisher = {{Springer-Verlag}}, title = {{{Local Search and the Traveling Salesman Problem: A Feature-Based Characterization of Problem Hardness}}}, year = {{2012}}, }