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Svensson, Magnus
Publications (10 of 14) Show all publications
Vachkov, G., Byttner, S. & Svensson, M. (2012). Battery Aging Detection Based on Sequential Clustering and Similarity Analysis. In: IS'2012: 2012 6th IEEE International Conference Intelligent Systems, Proceedings. Paper presented at 6th IEEE International Conference Intelligent Systems, IS 2012, Sofia, Bulgaria, 6-8 September, 2012 (pp. 42-47). Piscataway, N.J.: IEEE Press, Article ID 6335112.
Open this publication in new window or tab >>Battery Aging Detection Based on Sequential Clustering and Similarity Analysis
2012 (English)In: IS'2012: 2012 6th IEEE International Conference Intelligent Systems, Proceedings, Piscataway, N.J.: IEEE Press, 2012, p. 42-47, article id 6335112Conference paper, Published paper (Refereed)
Abstract [en]

The battery cells are an important part of electric and hybrid vehicles and their deterioration due to aging directly affects the life cycle and performance of the whole battery system. Therefore an early aging detection of the battery cell is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for battery aging detection, based on available data chunks from real operation of the vehicle. The first step is to aggregate (reduce) the original large amount of data by much smaller number of cluster centers. This is done by a newly proposed sequential clustering algorithm that arranges the clusters in decreasing order of their volumes. The next step is the proposed fuzzy inference procedure for weighed approximation of the cluster centers that creates comparable one dimensional fuzzy model for each available data set. Finally, the detection of the aged battery is treated as a similarity analysis problem, in which the pair distances between all battery cells are estimated by analyzing the predicted values from the respective fuzzy models. All these three steps of the computational procedure are explained in the paper and applied to real experimental data for battery aging detection. The results are positive and suggestions for further improvements are made in the conclusions. © 2012 IEEE.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2012
Keywords
battery aging detection, data aggregation, fuzzy inference, sequential clustering, similarity analysis, weighted approximation
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-19512 (URN)10.1109/IS.2012.6335112 (DOI)2-s2.0-84869838414 (Scopus ID)978-1-4673-2278-2 (ISBN)978-1-4673-2276-8 (ISBN)978-1-4673-2277-5 (ISBN)978-1-4673-2276-8 (ISBN)
Conference
6th IEEE International Conference Intelligent Systems, IS 2012, Sofia, Bulgaria, 6-8 September, 2012
Available from: 2012-09-07 Created: 2012-09-07 Last updated: 2018-03-22Bibliographically approved
Karginova, N., Byttner, S. & Svensson, M. (2012). Data-driven methods for classification of driving styles in buses. In: : . Paper presented at SAE 2012 World Congress & Exhibition, Cobo Center, Detroit, Michigan, USA, April 24-26, 2012. Warrendale, PA: SAE International
Open this publication in new window or tab >>Data-driven methods for classification of driving styles in buses
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Fuel consumption and vehicle breakdown depend upon the driving style of the driver, for example, hard driving style leads to more wear and consequently more failures of vehicle components. Because of this, it is important to identify and classify the driver’s driving style in order to give the driver feedback through a driver assistance system. The driver would then be able to detect and learn to avoid a driving style that is not appropriate. The input data is provided by different sensors installed in the vehicle, where different drivers and driving routes have been measured. The data is subjectively classified into two different driving styles: normal and hard. Hard driving style can be characterized, for example, by rapid acceleration and braking. Since it is not trivial to build a model which is able to distinguish hard driving from normal, a data mining approach has been employed. In the paper, several classifiers are compared (including e.g. neural networks and decision trees) and a discussion is made on the advantages and disadvantages of the different methods. Copyright © 2012 SAE International.

Place, publisher, year, edition, pages
Warrendale, PA: SAE International, 2012
Series
SAE Technical Papers, ISSN 0148-7191 ; 2012-01-0744
Keywords
Data-driven methods, Driver assistance system, Driving styles, Input datas, Vehicle components
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-19513 (URN)10.4271/2012-01-0744 (DOI)2-s2.0-84877176797 (Scopus ID)
Conference
SAE 2012 World Congress & Exhibition, Cobo Center, Detroit, Michigan, USA, April 24-26, 2012
Available from: 2012-09-07 Created: 2012-09-07 Last updated: 2018-03-22Bibliographically approved
Byttner, S., Rögnvaldsson, T. & Svensson, M. (2011). Consensus self-organized models for fault detection (COSMO). Engineering applications of artificial intelligence, 24(5), 833-839
Open this publication in new window or tab >>Consensus self-organized models for fault detection (COSMO)
2011 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 24, no 5, p. 833-839Article in journal (Refereed) Published
Abstract [en]

Methods for equipment monitoring are traditionally constructed from specific sensors and/or knowledge collected prior to implementation on the equipment. A different approach is presented here that builds up knowledge over time by exploratory search among the signals available on the internal field-bus system and comparing the observed signal relationships among a group of equipment that perform similar tasks. The approach is developed for the purpose of increasing vehicle uptime, and is therefore demonstrated in the case of a city bus and a heavy duty truck. However, it also works fine for smaller mechatronic systems like computer hard-drives. The approach builds on an onboard self-organized search for models that capture relations among signal values on the vehicles’ data buses, combined with a limited bandwidth telematics gateway and an off-line server application where the parameters of the self-organized models are compared. The presented approach represents a new look at error detection in commercial mechatronic systems, where the normal behavior of a system is actually found under real operating conditions, rather than the behavior observed in a number of laboratory tests or test-drives prior to production of the system. The approach has potential to be the basis for a self-discovering system for general purpose fault detection and diagnostics.

Place, publisher, year, edition, pages
Oxford: Pergamon Press, 2011
Keywords
Fault detection, Fleet management, Remote maintenance, Self-organizing systems, Telematics
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-15085 (URN)10.1016/j.engappai.2011.03.002 (DOI)000291524200010 ()2-s2.0-79956149234 (Scopus ID)
Available from: 2011-05-13 Created: 2011-05-13 Last updated: 2018-03-23Bibliographically approved
Byttner, S., Svensson, M. & Vachkov, G. (2011). Incremental classification of process data for anomaly detection based on similarity analysis. In: EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems : April 11-15, 2011, Paris, France. Paper presented at Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011, Paris, France, 11 - 15 April 2011, Category number CFP1114N-ART, Code85920 (pp. 108-115). Piscataway, N.J.: IEEE Press
Open this publication in new window or tab >>Incremental classification of process data for anomaly detection based on similarity analysis
2011 (English)In: EAIS 2011: 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems : April 11-15, 2011, Paris, France, Piscataway, N.J.: IEEE Press, 2011, p. 108-115Conference paper, Published paper (Refereed)
Abstract [en]

Performance evaluation and anomaly detection in complex systems are time consuming tasks based on analyzing, similarity analysis and classification of many different data sets from real operations. This paper presents an original computational technology for unsupervised incremental classification of large data sets by using a specially introduced similarity analysis method. First of all the so called compressed data models are obtained from the original large data sets by a newly proposed sequential clustering algorithm. Then the datasets are compared by pairs not directly, but by using their respective compressed data models. The evaluation of the pairs is done by a special similarity analysis method that uses the so called Intelligent Sensors (Agents) and data potentials. Finally a classification decision is generated by using a predefined threshold of similarity. The applicability of the proposed computational scheme for anomaly detection, based on many available large data sets is demonstrated on an example of 18 synthetic data sets. Suggestions for further improvements of the whole computation technology and a better applicability are also discussed in the paper.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2011
Keywords
anomaly detection, compressed data models, incremental classification, intelligent sensors, sequential clustering, similarity analysis
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hh:diva-14597 (URN)10.1109/EAIS.2011.5945928 (DOI)2-s2.0-80051492287 (Scopus ID)978-142449979-3 (ISBN)
Conference
Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE 5th Workshop on Evolving and Adaptive Intelligent Systems, EAIS 2011, Paris, France, 11 - 15 April 2011, Category number CFP1114N-ART, Code85920
Available from: 2011-03-17 Created: 2011-03-17 Last updated: 2018-03-23Bibliographically approved
Mosallam, A., Byttner, S., Svensson, M. & Rögnvaldsson, T. (2011). Nonlinear relation mining for maintenance prediction. Paper presented at IEEE Aerospace conference 2011, 5-12 march. New York: IEEE Press
Open this publication in new window or tab >>Nonlinear relation mining for maintenance prediction
2011 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a method for mining nonlinear relationships in machine data with the purpose of using such relationships to detect faults, isolate faults and predict wear and maintenance needs. The method is based on the symmetrical uncertainty measure from information theory, hierarchical clustering and self-organizing maps. It is demonstrated on synthetic data sets where it is shown to be able to detect interesting signal relations and outperform linear methods. It is also demonstrated on real data sets where it is considerably harder to select small feature sets. It is also demonstrated on the real data sets that there is information about system wear and system faults in the detected relationships. The work is part of a long-term research project with the aim to construct a self-organizing autonomic computing system for self-monitoring of mechatronic systems.

Place, publisher, year, edition, pages
New York: IEEE Press, 2011
Keywords
fault detection, fault isolation, hierarchical clustering, information theory, machine data mining, maintenance prediction, mechatronic system, nonlinear relation mining, self organizing autonomic computing system, self organizing map, symmetrical uncertainty measurement, wear prediction
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-14596 (URN)10.1109/AERO.2011.5747581 (DOI)2-s2.0-79955787404 (Scopus ID)978-1-4244-7350-2 (ISBN)
Conference
IEEE Aerospace conference 2011, 5-12 march
Note

©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2011-03-17 Created: 2011-03-17 Last updated: 2018-03-23Bibliographically approved
Svensson, M., Rögnvaldsson, T., Byttner, S., West, M. & Andersson, B. (2011). Unsupervised deviation detection by GMM - A simulation study. In: SDEMPED 2011: 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives : September 5-8, 2011, Bologna, Italy. Paper presented at 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2011, Bologna, Italy, 5-8 September, 2011. Piscataway, N.J.: IEEE Press
Open this publication in new window or tab >>Unsupervised deviation detection by GMM - A simulation study
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2011 (English)In: SDEMPED 2011: 8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics & Drives : September 5-8, 2011, Bologna, Italy, Piscataway, N.J.: IEEE Press, 2011Conference paper, Published paper (Refereed)
Abstract [en]

A new approach to improve fault detection of electrical machines is proposed. The increased usage of electrical machines and the higher demands on their availability requires new approaches to fault detection. In this paper we demonstrate that it is possible to detect a certain fault on a PMSM (Permanent Magnet Synchronous Machine) by using multiple similar motors, or a single motor, to build a norm of expected behavior by monitoring signal relations. This means that the machine is monitored in an unsupervised way. Four levels of an increased temperature in the rotor magnets have been investigated. The results are based on simulations and the signals used (for relation measurements) are available in a real motor installation. The method shows promising results in detecting two of the temperature faults. © 2011 IEEE.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2011
Keywords
Data mining, fault detection, machine learning, mechatronics, PMSM
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-16167 (URN)10.1109/DEMPED.2011.6063601 (DOI)2-s2.0-81255176446 (Scopus ID)978-142449303-6 (ISBN)
Conference
8th IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2011, Bologna, Italy, 5-8 September, 2011
Available from: 2011-09-06 Created: 2011-09-06 Last updated: 2018-03-22Bibliographically approved
Byttner, S., Rögnvaldsson, T. & Svensson, M. (2010). Finding the odd-one-out in fleets of mechatronic systems using embedded intelligent agents. In: Embedded reasoning: intelligence in embedded systems : papers from the AAAI Spring Symposium. Paper presented at 2010 AAAI Spring Symposium, Stanford University, Stanford, CA, United States, 22 - 24 March 2010, Code 81902 (pp. 17-19). Menlo Park, California: AAAI Press
Open this publication in new window or tab >>Finding the odd-one-out in fleets of mechatronic systems using embedded intelligent agents
2010 (English)In: Embedded reasoning: intelligence in embedded systems : papers from the AAAI Spring Symposium, Menlo Park, California: AAAI Press, 2010, p. 17-19Conference paper, Published paper (Refereed)
Abstract [en]

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.

Place, publisher, year, edition, pages
Menlo Park, California: AAAI Press, 2010
Series
Technical report (American Association for Artificial Intelligence) ; SS-10-04
Keywords
Agent design, Automated monitoring, Central servers, Commercial vehicles, Fault isolation, Human supervision, Mechatronic systems, New applications, Potential applications, State representation, Wireless communications
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-14023 (URN)2-s2.0-77957976013 (Scopus ID)978-1-57735-458-1 (ISBN)
Conference
2010 AAAI Spring Symposium, Stanford University, Stanford, CA, United States, 22 - 24 March 2010, Code 81902
Available from: 2010-12-17 Created: 2010-12-17 Last updated: 2018-03-23Bibliographically approved
Svensson, M., Forsberg, M., Byttner, S. & Rögnvaldsson, T. (2009). Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor. In: M.H. Hamza (Ed.), Proceeding Intelligent Systems and Control (ISC 2009). Paper presented at Intelligent Systems and Control 2009, Cambridge, Mass., USA, Nov. 2-4, 2009. Cambridge, Mass.: ACTA Press
Open this publication in new window or tab >>Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor
2009 (English)In: Proceeding Intelligent Systems and Control (ISC 2009) / [ed] M.H. Hamza, Cambridge, Mass.: ACTA Press, 2009Conference paper, Published paper (Refereed)
Abstract [en]

A new approach to improve fault detection is proposed. The method takes benefit of using a population of systems to dynamically define a norm of how the system works. The norm is derived from self-organizing algorithms which generate a low dimensional representation of how selected feature data are correlated. The feature data is selected from the state variables and from the control signals. The self-organizing method and limited number of feature signals enable fast deviation detection and low computational footprint on each system to be analyzed. The comparison analysis between the systems is performed at a service centre, to where the low-dimensional representations of the systems are transmitted. The method is demonstrated on a simulated DC-motor and the results are promising for deviation detection.

Place, publisher, year, edition, pages
Cambridge, Mass.: ACTA Press, 2009
Keywords
Machine learning, fault diagnosis, data mining, mechatronics, deviation detection, state variables
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-5024 (URN)2-s2.0-77952417209 (Scopus ID)978-0-88986-814-4 (ISBN)
Conference
Intelligent Systems and Control 2009, Cambridge, Mass., USA, Nov. 2-4, 2009
Note

Track: 665-086

Available from: 2010-06-28 Created: 2010-06-28 Last updated: 2018-03-23Bibliographically approved
Byttner, S., Rögnvaldsson, T., Svensson, M., Bitar, G. & Chominsky, W. (2009). Networked vehicles for automated fault detection. In: Guo li Chenggong da xue (Ed.), 2009 IEEE International Symposium on Circuits and Systems: circuits and systems for human centric smart living technologies, conference program, Taipei International Convention Center, Taipei, Taiwan, May 24-May 27, 2009. Paper presented at 2009 International Symposium on Circuits and Systems, May 24-27, Taipei, Taiwan (pp. 1213-1216). Piscataway, N.J.: IEEE Press
Open this publication in new window or tab >>Networked vehicles for automated fault detection
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2009 (English)In: 2009 IEEE International Symposium on Circuits and Systems: circuits and systems for human centric smart living technologies, conference program, Taipei International Convention Center, Taipei, Taiwan, May 24-May 27, 2009 / [ed] Guo li Chenggong da xue, Piscataway, N.J.: IEEE Press, 2009, p. 1213-1216Conference paper, Published paper (Refereed)
Abstract [en]

Creating fault detection software for complex mechatronic systems (e.g. modern vehicles) is costly both in terms of engineer time and hardware resources. With the availability of wireless communication in vehicles, information can be transmitted from vehicles to allow historical or fleet comparisons. New networked applications can be created that, e.g., monitor if the behavior of a certain system in a vehicle deviates compared to the system behavior observed in a fleet. This allows a new approach to fault detection that can help reduce development costs of fault detection software and create vehicle individual service planning. The COSMO (consensus self-organized modeling) methodology described in this paper creates a compact representation of the data observed for a subsystem or component in a vehicle. A representation that can be sent to a server in a backoffice and compared to similar representations for other vehicles. The backoffice server can collect representations from a single vehicle over time or from a fleet of vehicles to define a norm of the vehicle condition. The vehicle condition can then be monitored, looking for deviations from the norm. The method is demonstrated for measurements made on a real truck driven in varied conditions with ten different generated faults. The proposed method is able to detect all cases without prior information on what a fault looks like or which signals to use.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2009
Keywords
automated fault detection software, backoffice server, consensus self-organized modeling methodology, data mining, mechatronic system, network servers, networked vehicles, radio networks, software fault tolerance, telecommunication computing, traffic engineering computing, vehicle condition, wireless communication
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-5023 (URN)10.1109/ISCAS.2009.5117980 (DOI)000275929800311 ()2-s2.0-70350142425 (Scopus ID)978-1-4244-3827-3 (ISBN)
Conference
2009 International Symposium on Circuits and Systems, May 24-27, Taipei, Taiwan
Note

©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2010-06-28 Created: 2010-06-28 Last updated: 2018-03-23Bibliographically approved
Svensson, M., Byttner, S. & Rögnvaldsson, T. (2009). Vehicle Diagnostics Method by Anomaly Detection and Fault Identification Software. SAE international journal of passenger cars : electronic and electrical systems, 2(1), 352-358
Open this publication in new window or tab >>Vehicle Diagnostics Method by Anomaly Detection and Fault Identification Software
2009 (English)In: SAE international journal of passenger cars : electronic and electrical systems, ISSN 1946-4614, Vol. 2, no 1, p. 352-358Article in journal (Refereed) Published
Abstract [en]

A new approach is proposed for fault detection. It builds on using the relationships between sensor values on vehicles to detect deviating sensor readings and trends in the system performance. However, in contrast to previous approaches based on such sensor relations, our approach uses a fleet of vehicles to define the normal conditions and relations. The relationships between the sensors are also determined automatically in a self-organized way on each vehicle, i.e. no off-line modeling is required. The proposed method is the first step in a remote diagnostics and maintenance service where error detection is done automatically, followed by a download of special purpose diagnostics software for the particular subsystem where the possible fault was detected.

Place, publisher, year, edition, pages
Warrendale, PA: SAE international, 2009
Keywords
Vehicle Diagnostics
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-5018 (URN)2-s2.0-77953147742 (Scopus ID)
Available from: 2010-06-28 Created: 2010-06-28 Last updated: 2018-03-23Bibliographically approved

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