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  • 1.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Technology, SE-405 08 Göteborg, Sweden.
    Consensus self-organized models for fault detection (COSMO)2011In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 24, no 5, p. 833-839Article in journal (Refereed)
    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.

  • 2.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Rögnvaldsson, Thorsteinn
    AASS, Örebro University, 701 82 Örebro, Sweden.
    Svensson, Magnus
    Volvo Technology, SE-405 08 Göteborg, Sweden.
    Finding the odd-one-out in fleets of mechatronic systems using embedded intelligent agents2010In: Embedded reasoning: intelligence in embedded systems : papers from the AAAI Spring Symposium, Menlo Park, California: AAAI Press, 2010, p. 17-19Conference 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.

  • 3.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Technology, Göteborg, Sweden.
    Modeling for Vehicle Fleet Remote Diagnostics2007In: Proceedings of SAE 2007 Commercial Vehicle Engineering Congress, Warrendale, PA: SAE Inc. , 2007Conference paper (Refereed)
    Abstract [en]

    Quality and up-time management of vehicles is today receiving much attention from vehicle manufacturers. One of the reasons is that there is a desire to avoiding on-road failures to addressing potential issues during routine maintenance intervals or at times more convenient to the operator. Forthcoming telematic platforms and advanced diagnostic algorithms can enable the possibility to proactively handle problems and minimize stops. The platforms bring the possibility of increasing knowledge of fault characteristics and making diagnostic decisions by using a population of vehicles. However, this requires real-time diagnostic algorithms that process data both onboard and offboard at a central server. The paper presents a self organizing approach for failure and deviation detection on a fleet of vehicles. The approach builds on using parametric models for encoding the characteristical relations between different sensor readings for a vehicle sub-system or component. The models are low-dimensional representations of the operating characteristics of a sub-system or component and are possible to transfer over a limited wireless communication channel. The approach is demonstrated on simulated data of an electronically controlled suspension system for detecting a slow valve and a leaking bellow.

  • 4.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Technology, 405 08 Göteborg, Sweden.
    Self-organized Modeling for Vehicle Fleet Based Fault Detection2008In: Proceedings of the SAE World Congress & Exhibition, Warrendale, PA: SAE Inc. , 2008Conference paper (Refereed)
    Abstract [en]

    Operators of fleets of vehicles desire the best possible availability and usage of their vehicles. This means the preference is that maintenance of a vehicle is scheduled with as long intervals as possible. However, it is then important to be able to detect if a component in a specific vehicle is not functioning properly earlier than expected (due to e.g. manufacturing variations). This paper proposes a telematic based fault detection scheme for enabling fault detection for diagnostics by using a population of vehicles. The basic idea is that it is possible to create low-dimensional representations of a sub-system or component in a vehicle, where the representation (or model parameters) of a vehicle can be monitored for changes compared to the model parameters observed in a fleet of vehicles. If a model in a vehicle is found to deviate compared to a group of models from a fleet of vehicles, then the vehicle is judged to need diagnostics for that component (assuming the deviation in the model cannot be attributed to e.g. a different driver behavior). The representation should be low-dimensional so it is possible to have it transferred over a limited wireless communication channel to a communications center where the comparison is made. The algorithm is shown to be able to detect leakage on simulated data from a cooling system, work is currently in progress for detecting other types of faults.

  • 5.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Technology, SE-405 08 Göteborg, Sweden.
    Bitar, George
    Volvo Technology of America, 7825 National Service Rd., Greensboro, NC 27409, United States.
    Chominsky, Wesley
    Volvo Trucks North America, 7900 National Service Rd., Greensboro, NC 27409, United States.
    Networked vehicles for automated fault detection2009In: 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 (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.

  • 6.
    Byttner, Stefan
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Technology, 405 08 Göteborg, Sweden.
    Vachkov, Gancho
    Reliability-based Information Systems Engineering, Kagawa University, 761-0396 Kagawa, Japan.
    Incremental classification of process data for anomaly detection based on similarity analysis2011In: 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 (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.

  • 7.
    Karginova, Nadezda
    et al.
    Petrozavodsk University, Petrozavodsk, Russia.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Data-driven methods for classification of driving styles in buses2012Conference 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.

  • 8.
    Mosallam, Ahmed
    et al.
    Örebro University.
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Svensson, Magnus
    Volvo Technology.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent Systems´ laboratory.
    Nonlinear relation mining for maintenance prediction2011Conference 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.

  • 9.
    Rögnvaldsson, Thorsteinn
    et al.
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Panholzer, Georg
    Salzburg Research Advanced Networking Center Jakob-Haringer-Str. 51111 5020, Salzburg, Austria.
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Technology, 405 08 Goteborg, Sweden.
    A self-organized approach for unsupervised fault detection in multiple systems2008In: 19th International Conference on Pattern Recognition: (ICPR 2008) ; Tampa, Florida, USA 8-11 December 2008, Piscataway, N.J.: IEEE Press, 2008, p. 1-4Conference paper (Refereed)
    Abstract [en]

    An approach is proposed for automatic fault detection in a population of mechatronic systems. The idea is to employ self-organizing algorithms that produce low-dimensional representations of sensor and actuator values on the vehicles, and compare these low-dimensional representations among the systems. If a representation in one vehicle is found to deviate from, or to be not so similar to, the representations for the majority of the vehicles, then the vehicle is labeled for diagnostics. The presented approach makes use of principal component coding and a measure of distance between linear sub-spaces. The method is successfully demonstrated using simulated data for a commercial vehiclepsilas engine coolant system, and using real data for computer hard drives.

  • 10.
    Svensson, Magnus
    et al.
    Volvo Technology, 405 08 Göteborg, Sweden.
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Self-organizing maps for automatic fault detection in a vehicle cooling system2008In: 4th International IEEE Conference Intelligent Systems, 2008. IS '08, Piscataway, N.J.: IEEE Press, 2008, p. 24-8-24-12Conference paper (Refereed)
    Abstract [en]

    A telematic based system for enabling automatic fault detection of a population of vehicles is proposed. To avoid sending huge amounts of data over the telematics gateway, the idea is to use low-dimensional representations of sensor values in sub-systems in a vehicle. These low-dimensional representations are then compared between similar systems in a fleet. If a representation in a vehicle is found to deviate from the group of systems in the fleet, then the vehicle is labeled for diagnostics for that subsystem. The idea is demonstrated on the engine coolant system and it is shown how this self-organizing approach can detect varying levels of clogged radiator.

  • 11.
    Svensson, Magnus
    et al.
    Volvo Technology, Sweden.
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Rögnvaldsson, Thorsteinn
    AASS Örebro University, Sweden.
    Vehicle Diagnostics Method by Anomaly Detection and Fault Identification Software2009In: SAE international journal of passenger cars : electronic and electrical systems, ISSN 1946-4614, Vol. 2, no 1, p. 352-358Article in journal (Refereed)
    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.

  • 12.
    Svensson, Magnus
    et al.
    Volvo Technology.
    Forsberg, Magnus
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS).
    Deviation Detection by Self-Organized On-Line Models Simulated on a Feed-Back Controlled DC-Motor2009In: Proceeding Intelligent Systems and Control (ISC 2009) / [ed] M.H. Hamza, Cambridge, Mass.: ACTA Press, 2009Conference 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.

  • 13.
    Svensson, Magnus
    et al.
    Volvo Technology.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Byttner, Stefan
    Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    West, Martin
    Volvo Technology.
    Andersson, Björn
    Volvo Technology.
    Unsupervised deviation detection by GMM - A simulation study2011In: 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 (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.

  • 14.
    Vachkov, Gancho
    et al.
    Yamaguchi University, Yamaguchi, Japan.
    Byttner, Stefan
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligent systems (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Battery Aging Detection Based on Sequential Clustering and Similarity Analysis2012In: IS'2012: 2012 6th IEEE International Conference Intelligent Systems, Proceedings, Piscataway, N.J.: IEEE Press, 2012, p. 42-47, article id 6335112Conference 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.

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