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Byttner, Stefan
Publications (10 of 51) Show all publications
Sjöberg, J., Byttner, S., Wärnestål, P., Burgos, J. & Holmén, M. (2023). Promoting Life-Long Learning Through Flexible Educational Format for Professionals Within AI, Design and Innovation Management. In: Eva Brooks; Jeanette Sjöberg; Anders Kalsgaard Møller; Emma Edstrand (Ed.), Design, Learning, and Innovation: 7th EAI International Conference, DLI 2022, Faro, Portugal, November 21–22, 2022, Proceedings. Paper presented at Design, Learning, and Innovation: 7th EAI International Conference, DLI 2022, Faro, Portugal, November 21–22, 2022 (pp. 38-47). Cham: Springer
Open this publication in new window or tab >>Promoting Life-Long Learning Through Flexible Educational Format for Professionals Within AI, Design and Innovation Management
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2023 (English)In: Design, Learning, and Innovation: 7th EAI International Conference, DLI 2022, Faro, Portugal, November 21–22, 2022, Proceedings / [ed] Eva Brooks; Jeanette Sjöberg; Anders Kalsgaard Møller; Emma Edstrand, Cham: Springer, 2023, p. 38-47Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, the concept of lifelong learning has been emphasized in relation to higher education, with a bearing idea of the possibility for the individual for a continuous, self-motivated pursuit of gaining knowledge for both personal and professional reasons, provided by higher education institutions (HEI:s). But how can this actually be done in practice? In this paper we present an ongoing project called MAISTR, which is a collaboration between Swedish HEI:s and industry with the aim of providing a number of flexible courses within the subjects of Artificial intelligence (AI), Design, and Innovation management, for professionals. Our aim is to describe how the project is setup to create new learning opportunities, including the development process and co-creation with industry, the core structure and the pedagogical design. Furthermore, we would like to discuss both challenges and opportunities that come with this kind of project, as well as reflecting on early stage outcomes. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, ISSN 1867-8211, E-ISSN 1867-822X ; 493
Keywords
AI education, Flexible education, Learning for professionals, Lifelong learning, Pedagogical design
National Category
Other Social Sciences not elsewhere specified
Research subject
Smart Cities and Communities, LeaDS - Learning in a Digitalised Society
Identifiers
urn:nbn:se:hh:diva-50399 (URN)10.1007/978-3-031-31392-9_3 (DOI)2-s2.0-85161436060 (Scopus ID)978-3-031-31391-2 (ISBN)978-3-031-31392-9 (ISBN)
Conference
Design, Learning, and Innovation: 7th EAI International Conference, DLI 2022, Faro, Portugal, November 21–22, 2022
Available from: 2023-05-02 Created: 2023-05-02 Last updated: 2023-07-06Bibliographically approved
Etminani, K., Soliman, A., Byttner, S. & Miguel, O.-F. (2022). A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET. European Journal of Nuclear Medicine and Molecular Imaging, 49(2), 563-584
Open this publication in new window or tab >>A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer’s disease, and mild cognitive impairment using brain 18F-FDG PET
2022 (English)In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 49, no 2, p. 563-584Article in journal (Refereed) Published
Abstract [en]

Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers.

Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention.

Results: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders.

Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus. © 2021. The Author(s).

Place, publisher, year, edition, pages
New York: Springer-Verlag New York, 2022
Keywords
Alzheimer’s disease, Artificial intelligence, Deep learning, Dementia with Lewy bodies, FDG PET, Mild cognitive impairment
National Category
Neurology Computer Systems
Identifiers
urn:nbn:se:hh:diva-45392 (URN)10.1007/s00259-021-05483-0 (DOI)000679613100002 ()34328531 (PubMedID)2-s2.0-85111504097 (Scopus ID)
Funder
NIH (National Institute of Health), U01 AG024904Vinnova, 2017–02447Swedish Energy Agency
Note

Published online 30 July 2021. Funding text 1 Part of data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012). Funding text 2 Open access funding provided by Halmstad University. This study was part of a collaborative project between Center for Applied Intelligent System Research (CAISR) at Halmstad University, Sweden, and Department of Clinical Physiology, Department of Radiology and the Center for Medical Imaging Visualization (CMIV) at Linköping University Hospital, Sweden, and the European DLB consortium, which was funded by Analytic Imaging Diagnostics Arena (AIDA) initiative, jointly supported by VINNOVA (Grant 2017–02447), Formas and the Swedish Energy Agency. VG was supported by the Swiss National Science Foundation (projects 320030_169876, 320030_185028) and the Velux Foundation (project 1123). RB is a senior postdoctoral fellow of the Flanders Research Foundation (FWO 12I2121N).

Available from: 2021-08-17 Created: 2021-08-17 Last updated: 2022-10-31Bibliographically approved
Farouq, S., Byttner, S., Bouguelia, M.-R. & Gadd, H. (2022). A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating. Expert systems with applications, 201, Article ID 116864.
Open this publication in new window or tab >>A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating
2022 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 201, article id 116864Article in journal (Refereed) Published
Abstract [en]

The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and subfleet-level models to observe the behavior of a target unit. However, such infrastructure has to confront several challenges. First, from an anomaly detection perspective of monitoring a target unit, unit-level and subfleet-level models can give different information about the nature of an anomaly, and which approach or level model is appropriate is not always clear. Second, in the absence of well-understood prior models of unit and subfleet behavior, the choice of a base model at their respective levels, especially in an online/streaming setting, may not be clear. Third, managing false alarms is a major problem. To deal with these challenges, we proposed to rely on the conformal anomaly detection framework. In addition, an ensemble approach was deployed to mitigate the knowledge gap in understanding the underlying data-generating process at the unit and subfleet levels. Therefore, to monitor the behavior of a target unit, a unit-level ensemble model (ULEM) and a subfleet-level ensemble model (SLEM) were constructed, where each member of the respective ensemble is based on a conformal anomaly detector (CAD). However, since the information obtained by these two ensemble models through their p-values may not always agree, a combined ensemble model (CEM) was proposed. The results are based on real-world operational data obtained from district heating (DH) substations. Here, it was observed that CEM reduces the overall false alarms compared to ULEM or SLEM, albeit at the cost of some detection delay. The analysis demonstrated the advantages and limitations of ULEM, SLEM, and CEM. Furthermore, discords obtained from the state-of-the-art matrix-profile (MP) method and the combined calibration scores obtained from ULEM and SLEM were compared in an offline setting. Here, it was observed that SLEM achieved a better overall precision and detection delay. Finally, the different components related to ULEM, SLEM, and CEM were put together into what we refer to as TRANTOR: a conformal anomaly detection based industrial fleet monitoring framework. The proposed framework is expected to enable fleet operators in various domains to improve their monitoring infrastructure by efficiently detecting anomalous behavior and controlling false alarms at the target units. © 2022

Place, publisher, year, edition, pages
Oxford: Elsevier, 2022
Keywords
Conformal anomaly detection, Fleet monitoring, Unit-level model, Subfleet-level model, Ensemble model, District heating substations
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-46273 (URN)10.1016/j.eswa.2022.116864 (DOI)000798741200007 ()2-s2.0-85129522080 (Scopus ID)
Funder
Knowledge Foundation, 20160103
Note

Som manuskript i avhandling / As manuscript in thesis

Available from: 2022-02-01 Created: 2022-02-01 Last updated: 2022-09-01Bibliographically approved
Soliman, A., Chang, J. R., Etminani, K., Byttner, S., Davidsson, A., Martínez-Sanchis, B., . . . Ochoa-Figueroa, M. (2022). Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model. BMC Medical Informatics and Decision Making, 22, 1-15, Article ID 318.
Open this publication in new window or tab >>Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model
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2022 (English)In: BMC Medical Informatics and Decision Making, E-ISSN 1472-6947, Vol. 22, p. 1-15, article id 318Article in journal (Refereed) Published
Abstract [en]

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones. © 2022, The Author(s).

Place, publisher, year, edition, pages
London: BioMed Central (BMC), 2022
Keywords
Brain Neurodegenerative Disorders, Convolution Neural Networks, Medical Image Classification, Transfer Learning
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-49079 (URN)10.1186/s12911-022-02054-7 (DOI)000904994900001 ()36476613 (PubMedID)2-s2.0-85143570393 (Scopus ID)
Available from: 2023-01-10 Created: 2023-01-10 Last updated: 2023-08-21Bibliographically approved
Farouq, S., Byttner, S., Bouguelia, M.-R. & Gadd, H. (2021). Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets. Neurocomputing, 462, 591-606
Open this publication in new window or tab >>Mondrian conformal anomaly detection for fault sequence identification in heterogeneous fleets
2021 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 462, p. 591-606Article in journal (Refereed) Published
Abstract [en]

We considered the case of monitoring a large fleet where heterogeneity in the operational behavior among its constituent units (i.e., systems or machines) is non-negligible, and no labeled data is available. Each unit in the fleet, referred to as a target, is tracked by its sub-fleet. A conformal sub-fleet (CSF) is a set of units that act as a proxy for the normal operational behavior of a target unit by relying on the Mondrian conformal anomaly detection framework. Two approaches, the k-nearest neighbors and conformal clustering, were investigated for constructing such a sub-fleet by formulating a stability criterion. Moreover, it is important to discover the sub-sequence of events that describes an anomalous behavior in a target unit. Hence, we proposed to extract such sub-sequences for further investigation without pre-specifying their length. We refer to it as a conformal anomaly sequence (CAS). Furthermore, different nonconformity measures were evaluated for their efficiency, i.e., their ability to detect anomalous behavior in a target unit, based on the length of the observed CAS and the S-criterion value. The CSF approach was evaluated in the context of monitoring district heating substations. Anomalous behavior sub-sequences were corroborated with the domain expert leading to the conclusion that the proposed approach has the potential to be useful for both diagnostic and knowledge extraction purposes, especially in domains where labeled data is not available or hard to obtain. © 2021

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2021
Keywords
Conformal anomaly detection, Conformal anomaly sequence (CAS), District heating, Sub-fleet based monitoring, Substation monitoring
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-45735 (URN)10.1016/j.neucom.2021.08.016 (DOI)000696933600013 ()2-s2.0-85115660223 (Scopus ID)
Available from: 2021-10-15 Created: 2021-10-15 Last updated: 2022-02-01Bibliographically approved
Etminani, K., Soliman, A., Byttner, S., Davidsson, A. & Ochoa-Figueroa, M. (2021). Peeking inside the box: Transfer Learning vs 3D convolutional neural networks applied in neurodegenerative diseases. In: Proceedings of CIBB 2021: . Paper presented at 2021 International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2021), 15–17 nov., 2021.
Open this publication in new window or tab >>Peeking inside the box: Transfer Learning vs 3D convolutional neural networks applied in neurodegenerative diseases
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2021 (English)In: Proceedings of CIBB 2021, 2021Conference paper, Published paper (Refereed)
Abstract [en]

Convolutional Neural Networks (CNNs) have shown their effectiveness in a variety of imaging applications including medical imaging diagnostics. However, these deep learning models are data-hungry and need enough labeled samples for the training phase which is limited in the medical domain. Transfer learning is one possible solution to this challenge with training a new model. Assessing model performance should be done not only based on criteria like accuracy, and area under the ROC curve, but also it is important to investigate what regions were of most interest for the classification decisions, especially for medical applications. We performed a case study on neurodegenerative disorders, in specific Alzheimer’s disease, mild cognitive im- pairment, dementia with lewy bodies and cognitively normal brains using 3D 18F-FDG-PET brain scans. Two transfer learning models, InceptionV3 and ResNet50, as well as a custom 3D-CNN that is trained from scratch are compared. Two XAI methods, occlusion and Grad-CAM are chosen to visualize the important brain regions using correctly classified cases. We found that the TL models learn significantly different decision surfaces than the 3D-CNN model. The 3D spatial structure of the brain regions are better kept in the 3D-CNN model, and that might explain the higher performance of this model over 2D-TL models. Moreover, we found out the two XAI methods provide different results, where occlusion method focused more on specific brain regions.

Keywords
Explainable artificial intelligence, neurodegenerative disease, transfer learning, occlusion, Grad-CAM
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-46942 (URN)
Conference
2021 International Conference on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2021), 15–17 nov., 2021
Available from: 2022-06-07 Created: 2022-06-07 Last updated: 2022-06-07Bibliographically approved
Farouq, S., Byttner, S., Bouguelia, M.-R., Nord, N. & Gadd, H. (2020). Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach. Engineering applications of artificial intelligence, 90, Article ID 103492.
Open this publication in new window or tab >>Large-scale monitoring of operationally diverse district heating substations: A reference-group based approach
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2020 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 90, article id 103492Article in journal (Refereed) Published
Abstract [en]

A typical district heating (DH) network consists of hundreds, sometimes thousands, of substations. In the absence of a well-understood prior model or data labels about each substation, the overall monitoring of such large number of substations can be challenging. To overcome the challenge, an approach based on the collective operational monitoring of each substation by a local group (i.e., the reference-group) of other similar substations in the network was formulated. Herein, if a substation of interest (i.e., the target) starts to behave differently in comparison to those in its reference-group, then it was designated as an outlier. The approach was demonstrated on the monitoring of the return temperature variable for atypical and faulty operational behavior in 778 substations associated with multi-dwelling buildings. The choice of an appropriate similarity measure along with its size k were the two important factors that enables a reference-group to effectively detect an outlier target. Thus, different similarity measures and size k for the construction of the reference-groups were investigated, which led to the selection of the Euclidean distance with = 80. This setup resulted in the detection of 77 target substations that were outliers, i.e., the behavior of their return temperature changed in comparison to the majority of those in their respective reference-groups. Of these, 44 were detected due to the local construction of the reference-groups. In addition, six frequent patterns of deviating behavior in the return temperature of the substations were identified using the reference-group based approach, which were then further corroborated by the feedback from a DH domain expert. © 2020 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2020
Keywords
District heating substations, Return temperature, Reference-group based operational monitoring, Fault detection, Outlier detection
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
urn:nbn:se:hh:diva-40962 (URN)10.1016/j.engappai.2020.103492 (DOI)000528194400012 ()2-s2.0-85078822459 (Scopus ID)
Funder
Knowledge Foundation, 20160103
Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2022-02-01Bibliographically approved
Farouq, S., Byttner, S. & Bouguelia, M.-R. (2018). On monitoring heat-pumps with a group-based conformal anomaly detection approach. In: Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr (Ed.), ICDATA' 18: Proceedings of the 2018 International Conference on Data Science. Paper presented at 2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA (pp. 63-69). CSREA Press
Open this publication in new window or tab >>On monitoring heat-pumps with a group-based conformal anomaly detection approach
2018 (English)In: ICDATA' 18: Proceedings of the 2018 International Conference on Data Science / [ed] Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, CSREA Press, 2018, p. 63-69Conference paper, Published paper (Refereed)
Abstract [en]

The ever increasing complexity of modern systems and equipment make the task of monitoring their health quite challenging. Traditional methods such as expert defined thresholds, physics based models and process history based techniques have certain drawbacks. Thresholds defined by experts require deep knowledge about the system and are often too conservative. Physics driven approaches are costly to develop and maintain. Finally, process history based models require large amount of data that may not be available at design time of a system. Moreover, the focus of these traditional approaches has been system specific. Hence, when industrial systems are deployed on a large scale, their monitoring becomes a new challenge. Under these conditions, this paper demonstrates the use of a group-based selfmonitoring approach that learns over time from similar systems subject to similar conditions. The approach is based on conformal anomaly detection coupled with an exchangeability test that uses martingales. This allows setting a threshold value based on sound theoretical justification. A hypothesis test based on this threshold is used to decide on if a system has deviated from its group. We demonstrate the feasibility of this approach through a real case study of monitoring a group of heat-pumps where it can detect a faulty hot-water switch-valve and a broken outdoor temperature sensor without previously observing these faults.

Place, publisher, year, edition, pages
CSREA Press, 2018
Keywords
group-based monitoring, nonconformity measure (NCM), martingale test
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40961 (URN)1-60132-481-2 (ISBN)9781601324818 (ISBN)
Conference
2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA
Available from: 2019-11-16 Created: 2019-11-16 Last updated: 2022-02-01Bibliographically approved
Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A. & Byttner, S. (2018). Ranking Abnormal Substations by Power Signature Dispersion. Paper presented at 16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018. Energy Procedia, 149, 345-353
Open this publication in new window or tab >>Ranking Abnormal Substations by Power Signature Dispersion
2018 (English)In: Energy Procedia, ISSN 1876-6102, Vol. 149, p. 345-353Article in journal (Refereed) Published
Abstract [en]

The relation between heat demand and outdoor temperature (heat power signature) is a typical feature used to diagnose abnormal heat demand. Prior work is mainly based on setting thresholds, either statistically or manually, in order to identify outliers in the power signature. However, setting the correct threshold is a difficult task since heat demand is unique for each building. Too loose thresholds may allow outliers to go unspotted, while too tight thresholds can cause too many false alarms.

Moreover, just the number of outliers does not reflect the dispersion level in the power signature. However, high dispersion is often caused by fault or configuration problems and should be considered while modeling abnormal heat demand.

In this work, we present a novel method for ranking substations by measuring both dispersion and outliers in the power signature. We use robust regression to estimate a linear regression model. Observations that fall outside of the threshold in this model are considered outliers. Dispersion is measured using coefficient of determination R2 which is a statistical measure of how close the data are to the fitted regression line.

Our method first produces two different lists by ranking substations using number of outliers and dispersion separately. Then, we merge the two lists into one using the Borda Count method. Substations appearing on the top of the list should indicate higher abnormality in heat demand compared to the ones on the bottom. We have applied our model on data from substations connected to two district heating networks in the south of Sweden. Three different approaches i.e. outlier-based, dispersion-based and aggregated methods are compared against the rankings based on return temperatures. The results show that our method significantly outperforms the state-of-the-art outlier-based method. © 2018 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Keywords
abnormal heat demand, district heating, anomaly detection, fault detection, power signature
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-38253 (URN)10.1016/j.egypro.2018.08.198 (DOI)000482873900036 ()2-s2.0-85054100441 (Scopus ID)
Conference
16th International Symposium on District Heating and Cooling, DHC2018, Hamburg, Germany, 9-12 September, 2018
Funder
Knowledge Foundation, 20160103
Available from: 2018-11-04 Created: 2018-11-04 Last updated: 2023-08-28Bibliographically approved
Rögnvaldsson, T., Nowaczyk, S., Byttner, S., Prytz, R. & Svensson, M. (2018). Self-monitoring for maintenance of vehicle fleets. Data mining and knowledge discovery, 32(2), 344-384
Open this publication in new window or tab >>Self-monitoring for maintenance of vehicle fleets
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2018 (English)In: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, no 2, p. 344-384Article in journal (Refereed) Published
Abstract [en]

An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g. a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost four years. © 2017 The Author(s)

Place, publisher, year, edition, pages
New York: Springer-Verlag New York, 2018
Keywords
Data Mining, Knowledge Discovery, Empirical Studies, Vehicle Fleet Maintenance
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-34746 (URN)10.1007/s10618-017-0538-6 (DOI)000426080300003 ()2-s2.0-85027693423 (Scopus ID)
Projects
ReDi2ServiceCAISR
Funder
VINNOVAKnowledge Foundation
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2020-02-03Bibliographically approved
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