hh.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Byttner, Stefan
Publications (10 of 43) Show all publications
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, E-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)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: 2018-11-12Bibliographically 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
Show others...
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)2-s2.0-85027693423 (Scopus ID)
Projects
ReDi2ServiceCAISR
Funder
VINNOVAKnowledge Foundation
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-02-27Bibliographically approved
Farouq, S., Byttner, S. & Gadd, H. (2018). Towards understanding district heating substation behavior using robust first difference regression. In: Energy Procedia: . Paper presented at 16th International Symposium on District Heating and Cooling, DHC 2018, 9-12 September, 2018 (pp. 236-245). Amsterdam: Elsevier, 149
Open this publication in new window or tab >>Towards understanding district heating substation behavior using robust first difference regression
2018 (English)In: Energy Procedia, Amsterdam: Elsevier, 2018, Vol. 149, p. 236-245Conference paper, Published paper (Refereed)
Abstract [en]

The behavior of a district heating (DH) substation has a social and operational context. The social context comes from its general usage pattern and personal requirements of building inhabitants. The operational context comes from its configuration settings which considers both the weather conditions and social requirements. The parameter estimating thermal energy demand response with respect to change in outdoor temperature conditions along with the strength of the relationship between these variables are two important measures of operational efficiency of a substation. In practice, they can be estimated using a regression model where the slope parameter measures the average response and R2 measures the strength of the relationship. These measures are also important from a monitoring perspective. However, factors related to the social context of a building and the presence of unexplained outliers can make the estimation of these measures a challenging task. Social context of a data point in DH, in many cases appears as an outlier. Data efficiency is also required if these measures are to be estimated in a timely manner. Under these circumstances, methods that can isolate and reduce the effect of outliers in a principled and data efficient manner are required. We therefore propose to use Huber regression, a robust method based on M-estimator type loss function. This method can not only identify possible outliers present in the data of each substation but also reduce their effect on the estimated slope parameter. Moreover, substations that are comparable according to certain criteria, for instance, those with almost identical energy demand levels, should have relatively similar slopes. This provides an opportunity to observe deviating substations under the assumption that comparable substations should show homogeneity in their behavior. Furthermore, the slope parameter can be compared across time to observe if the dynamics of a substation has changed. Our analysis shows that Huber regression in combination with ordinary least squares can provide reliable estimates on the operational efficiency of DH substations. © 2018 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2018
Series
Energy Procedia, E-ISSN 1876-6102 ; 149
Keywords
district heating, energy demand response, outliers, robust regression, substation control, Energy management, Regression analysis, Statistics, Energy demands, Heating substations, Operational efficiencies, Ordinary least squares, Parameter estimating, Robust regressions, Substation controls, Parameter estimation
National Category
Energy Systems
Identifiers
urn:nbn:se:hh:diva-38729 (URN)10.1016/j.egypro.2018.08.188 (DOI)2-s2.0-85054085009 (Scopus ID)
Conference
16th International Symposium on District Heating and Cooling, DHC 2018, 9-12 September, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Svensson, O., Thelin, S., Byttner, S. & Fan, Y. (2017). Indirect Tire Monitoring System - Machine Learning Approach. In: IOP Conference Series: Materials Science and Engineering: . Paper presented at 11th International Congress of Automotive and Transport Engineering: Mobility Engineering and Environment (CAR 2017), Pitesti, Romania, 8-10 November, 2017. Bristol: Institute of Physics Publishing (IOPP), 252, Article ID 012018.
Open this publication in new window or tab >>Indirect Tire Monitoring System - Machine Learning Approach
2017 (English)In: IOP Conference Series: Materials Science and Engineering, Bristol: Institute of Physics Publishing (IOPP), 2017, Vol. 252, article id 012018Conference paper, Published paper (Refereed)
Abstract [en]

The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an acceptable range over a period of time. These incidents will be notified through the cloud for the operator to analyze for upcoming maintenance decisions. © 2017 Published under licence by IOP Publishing Ltd.

Place, publisher, year, edition, pages
Bristol: Institute of Physics Publishing (IOPP), 2017
Series
IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X ; 252
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-35499 (URN)10.1088/1757-899X/252/1/012018 (DOI)2-s2.0-85034218557 (Scopus ID)
Conference
11th International Congress of Automotive and Transport Engineering: Mobility Engineering and Environment (CAR 2017), Pitesti, Romania, 8-10 November, 2017
Available from: 2017-11-29 Created: 2017-11-29 Last updated: 2017-12-11Bibliographically approved
Bouguelia, M.-R., Gonzalez, R., Iagnemma, K. & Byttner, S. (2017). Unsupervised classification of slip events for planetary exploration rovers. Journal of terramechanics, 73, 95-106
Open this publication in new window or tab >>Unsupervised classification of slip events for planetary exploration rovers
2017 (English)In: Journal of terramechanics, ISSN 0022-4898, E-ISSN 1879-1204, Vol. 73, p. 95-106Article in journal (Refereed) Published
Abstract [en]

This paper introduces an unsupervised method for the classification of discrete rovers' slip events based on proprioceptive signals. In particular, the method is able to automatically discover and track various degrees of slip (i.e. low slip, moderate slip, high slip). The proposed method is based on aggregating the data over time, since high level concepts, such as high and low slip, are concepts that are dependent on longer time perspectives. Different features and subsets of the data have been identified leading to a proper clustering, interpreting those clusters as initial models of the prospective concepts. Bayesian tracking has been used in order to continuously improve the parameters of these models, based on the new data. Two real datasets are used to validate the proposed approach in comparison to other known unsupervised and supervised machine learning methods. The first dataset is collected by a single-wheel testbed available at MIT. The second dataset was collected by means of a planetary exploration rover in real off-road conditions. Experiments prove that the proposed method is more accurate (up to 86% of accuracy vs. 80% for K-means) in discovering various levels of slip while being fully unsupervised (no need for hand-labeled data for training). © 2017 ISTVS

Place, publisher, year, edition, pages
Doetinchem: Elsevier, 2017
Keywords
Unsupervised learning, Clustering, Data-driven modeling, Slip, MSL rover, LATUV rover
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-35169 (URN)10.1016/j.jterra.2017.09.001 (DOI)2-s2.0-85029811187 (Scopus ID)
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2018-01-13Bibliographically approved
Gonzalez, R., Byttner, S. & Iagnemma, K. (2016). Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers. In: Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016.: . Paper presented at International Conference of the ISTVS (International Society for Terrain-Vehicle Systems), Detroit, Michigan, USA, 12-14 September, 2016.
Open this publication in new window or tab >>Comparison of Machine Learning Approaches for Soil Embedding Detection of Planetary Exploration Rovers
2016 (English)In: Proceedings of the 8th ISTVS Americas Conference, Detroit, September 12-14, 2016., 2016Conference paper, Published paper (Refereed)
Abstract [en]

This paper analyzes the advantages and limitations of known machine learning approaches to cope with the problem of incipient rover embedding detection based on propioceptive signals. In particular, two supervised learning approaches (Support Vector Machines and Feed-forward Neural Networks) are compared to two unsupervised learning approaches (K-means and Self-Organizing Maps) in order to identify various degrees of slip (e.g. low slip, moderate slip, high slip). A real dataset collected by a single-wheel testbed available at MIT has been used to validate each strategy. The SVM algorithm achieves the best performance (accuracy >95 %). However, the SOM algorithm represents a better solution in terms of accuracy and the need of hand-labeled data for training the classifier (accuracy >84 %).

Keywords
Support Vector Machine (SVM), Feed-forward Neural Network (FF-NN), K-means, Self-Organizing Map (SOM), Mars Science Laboratory (MSL) rover
National Category
Signal Processing Robotics
Identifiers
urn:nbn:se:hh:diva-32049 (URN)
Conference
International Conference of the ISTVS (International Society for Terrain-Vehicle Systems), Detroit, Michigan, USA, 12-14 September, 2016
Note

Funding: NASA

Available from: 2016-09-19 Created: 2016-09-19 Last updated: 2018-03-22Bibliographically approved
Uličný, M., Lundström, J. & Byttner, S. (2016). Robustness of Deep Convolutional Neural Networks for Image Recognition. In: Anabel Martin-Gonzalez, Victor Uc-Cetina (Ed.), Intelligent Computing Systems: First International Symposium, ISICS 2016, Mérida, México, March 16-18, 2016, Proceedings. Paper presented at First International Symposium, ISICS 2016, Mérida, México, March 16-18 2016 (pp. 16-30). Cham: Springer, 597
Open this publication in new window or tab >>Robustness of Deep Convolutional Neural Networks for Image Recognition
2016 (English)In: Intelligent Computing Systems: First International Symposium, ISICS 2016, Mérida, México, March 16-18, 2016, Proceedings / [ed] Anabel Martin-Gonzalez, Victor Uc-Cetina, Cham: Springer, 2016, Vol. 597, p. 16-30Conference paper, Published paper (Refereed)
Abstract [en]

Recent research has found deep neural networks to be vulnerable, by means of prediction error, to images corrupted by small amounts of non-random noise. These images, known as adversarial examples are created by exploiting the input to output mapping of the network. For the MNIST database, we observe in this paper how well the known regularization/robustness methods improve generalization performance of deep neural networks when classifying adversarial examples and examples perturbed with random noise. We conduct a comparison of these methods with our proposed robustness method, an ensemble of models trained on adversarial examples, able to clearly reduce prediction error. Apart from robustness experiments, human classification accuracy for adversarial examples and examples perturbed with random noise is measured. Obtained human classification accuracy is compared to the accuracy of deep neural networks measured in the same experimental settings. The results indicate, human performance does not suffer from neural network adversarial noise.

Place, publisher, year, edition, pages
Cham: Springer, 2016
Series
Communications in Computer and Information Science, ISSN 1865-0929
Keywords
Adversarial examples, Deep neural network, Noise robustness
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-31443 (URN)10.1007/978-3-319-30447-2_2 (DOI)000378489600002 ()2-s2.0-84960448659 (Scopus ID)978-3-319-30446-5 (ISBN)978-3-319-30447-2 (ISBN)
Conference
First International Symposium, ISICS 2016, Mérida, México, March 16-18 2016
Available from: 2016-06-28 Created: 2016-06-28 Last updated: 2018-03-22Bibliographically approved
Helldin, T., Riveiro, M., Pashami, S., Falkman, G., Byttner, S. & Slawomir, N. (2016). Supporting Analytical Reasoning: A Study from the Automotive Industry. In: Sakae Yamamoto (Ed.), Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II. Paper presented at 18th International Conference, HCI International 2016, Toronto, Canada, July 17-22, 2016 (pp. 20-31). Cham: Springer, 9735
Open this publication in new window or tab >>Supporting Analytical Reasoning: A Study from the Automotive Industry
Show others...
2016 (English)In: Human Interface and the Management of Information: Applications and Services: 18th International Conference, HCI International 2016: Toronto, Canada, July 17-22, 2016. Proceedings, Part II / [ed] Sakae Yamamoto, Cham: Springer, 2016, Vol. 9735, p. 20-31Conference paper, Published paper (Refereed)
Abstract [en]

In the era of big data, it is imperative to assist the human analyst in the endeavor to find solutions to ill-defined problems, i.e. to “detect the expected and discover the unexpected” (Yi et al., 2008). To their aid, a plethora of analysis support systems is available to the analysts. However, these support systems often lack visual and interactive features, leaving the analysts with no opportunity to guide, influence and even understand the automatic reasoning performed and the data used. Yet, to be able to appropriately support the analysts in their sense-making process, we must look at this process more closely. In this paper, we present the results from interviews performed together with data analysts from the automotive industry where we have investigated how they handle the data, analyze it and make decisions based on the data, outlining directions for the development of analytical support systems within the area. © Springer International Publishing Switzerland 2016.

Place, publisher, year, edition, pages
Cham: Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9735
Keywords
Analytical reasoning, Sense-making, Visual analytics, Truck data analysis, Big data
National Category
Computer Systems Signal Processing
Identifiers
urn:nbn:se:hh:diva-32048 (URN)10.1007/978-3-319-40397-7_3 (DOI)000389467600003 ()2-s2.0-84978877445 (Scopus ID)978-3-319-40396-0 (ISBN)978-3-319-40397-7 (ISBN)
Conference
18th International Conference, HCI International 2016, Toronto, Canada, July 17-22, 2016
Projects
BIDAF
Funder
Knowledge Foundation, BIDAF 2014/32
Available from: 2016-09-19 Created: 2016-09-19 Last updated: 2019-04-12Bibliographically approved
Prytz, R., Nowaczyk, S., Rögnvaldsson, T. & Byttner, S. (2015). Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data. Engineering applications of artificial intelligence, 41, 139-150
Open this publication in new window or tab >>Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data
2015 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 41, p. 139-150Article in journal (Refereed) Published
Abstract [en]

Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.

Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd.

Place, publisher, year, edition, pages
Oxford: Pergamon Press, 2015
Keywords
Machine Learning, Diagnostics, Fault Detection, Automotive Industry, Air Compressor
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-27808 (URN)10.1016/j.engappai.2015.02.009 (DOI)000353739800012 ()2-s2.0-84926374379 (Scopus ID)
Projects
in4uptime
Funder
VINNOVAKnowledge Foundation
Note

The authors thank Vinnova (Swedish Governmental Agency for Innovation Systems), AB Volvo, Halmstad University, and the Swedish Knowledge Foundation for financial support for doing this research.

Available from: 2015-02-16 Created: 2015-02-16 Last updated: 2018-03-22Bibliographically approved
Vachkov, G., Byttner, S. & Svensson, M. (2014). Detection of Deviation in Performance of Battery Cells by Data Compression and Similarity Analysis. International Journal of Intelligent Systems, 29(3), 207-222
Open this publication in new window or tab >>Detection of Deviation in Performance of Battery Cells by Data Compression and Similarity Analysis
2014 (English)In: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 29, no 3, p. 207-222Article in journal (Refereed) Published
Abstract [en]

The battery cells are an important part of electric and hybrid vehicles, and their deterioration due to aging or malfunction directly affects the life cycle and performance of the whole battery system. Therefore, an early detection of deviation in performance of the battery cells is an important task and its correct solution could significantly improve the whole vehicle performance. This paper presents a computational strategy for the detection of deviation of battery cells, due to aging or malfunction. The detection is based on periodically processing a predetermined number of data collected in data blocks that are obtained during the real operation of the vehicle. The first step is data compression, when the original large amount of data is reduced to 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 using a fuzzy inference procedure for weighted approximation of the cluster centers to create one-dimensional models for each battery cell that represents the voltage–current relationship. This creates an equal basis for the further comparison of the battery cells. Finally, the detection of the deviated battery cells is treated as a similarity-analysis problem, in which the pair distances between all battery cells are estimated by analyzing the estimations for voltage 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 the detection of deviation of five battery cells. Discussions and suggestions are made for a practical application aimed at designing a monitoring system for the detection of deviations. © 2013 Wiley Periodicals, Inc.

Place, publisher, year, edition, pages
Hoboken, NJ: John Wiley & Sons, 2014
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-24224 (URN)10.1002/int.21637 (DOI)000329145000001 ()2-s2.0-84891829937 (Scopus ID)
Note

Special Issue: Advances in Intelligent Systems

Available from: 2013-12-20 Created: 2013-12-20 Last updated: 2018-03-22Bibliographically approved
Organisations

Search in DiVA

Show all publications