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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
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
Prytz, R. (2014). Machine learning methods for vehicle predictive maintenance using off-board and on-board data. (Licentiate dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Machine learning methods for vehicle predictive maintenance using off-board and on-board data
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Vehicle uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. Traditional Fleet Management Systems are gradually extended with new features to improve reliability, such as better maintenance planning. Typical diagnostic and predictive maintenance methods require extensive experimentation and modelling during development. This is unfeasible if the complete vehicle is addressed as it would require too much engineering resources.

This thesis investigates unsupervised and supervised methods for predicting vehicle maintenance. The methods are data driven and use extensive amounts of data, either streamed, on-board data or historic and aggregated data from off-board databases. The methods rely on a telematics gateway that enables vehicles to communicate with a back-office system. Data representations, either aggregations or models, are sent wirelessly to an off-board system which analyses the data for deviations. These are later associated to the repair history and form a knowledge base that can be used to predict upcoming failures on other vehicles that show the same deviations.

The thesis further investigates different ways of doing data representations and deviation detection. The first one presented, COSMO, is an unsupervised and self-organised approach demonstrated on a fleet of city buses. It automatically comes up with the most interesting on-board data representations and uses a consensus based approach to isolate the deviating vehicle. The second approach outlined is a super-vised classification based on earlier collected and aggregated vehicle statistics in which the repair history is used to label the usage statistics. A classifier is trained to learn patterns in the usage data that precede specific repairs and thus can be used to predict vehicle maintenance. This method is demonstrated for failures of the vehicle air compressor and based on AB Volvo’s database of vehicle usage statistics.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2014. p. 96
Series
Halmstad University Dissertations ; 9
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-27869 (URN)978-91-87045-18-9 (ISBN)978-91-87045-17-2 (ISBN)
Presentation
2014-09-26, Haldasalen, Visionen, Halmstad University, Halmstad, 10:00 (English)
Opponent
Supervisors
Funder
VINNOVA
Available from: 2015-03-12 Created: 2015-02-19 Last updated: 2015-03-17Bibliographically approved
Byttner, S., Nowaczyk, S., Prytz, R. & Rögnvaldsson, T. (2013). A field test with self-organized modeling for knowledge discovery in a fleet of city buses. In: Shuxiang Guo (Ed.), 2013 IEEE International Conference on Mechatronics and Automation (ICMA 2013): . Paper presented at 10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013, Takamastu, Japan, 4-7 August, 2013 (pp. 896-901). Piscataway, NJ: IEEE Press, Article ID 6618034.
Open this publication in new window or tab >>A field test with self-organized modeling for knowledge discovery in a fleet of city buses
2013 (English)In: 2013 IEEE International Conference on Mechatronics and Automation (ICMA 2013) / [ed] Shuxiang Guo, Piscataway, NJ: IEEE Press, 2013, p. 896-901, article id 6618034Conference paper, Published paper (Refereed)
Abstract [en]

Fleets of commercial vehicles represent an excellent real life setting for ubiquitous knowledge discovery. There are many electronic control units onboard a modern bus or truck, with hundreds of signals being transmitted between them on the controller area network. The growing complexity of the vehicles has lead to a significant desire to have systems for fault detection, remote diagnostics and maintenance prediction. This paper aims to show that it is possible to discover useful diagnostic knowledge by a self-organized algorithm in the scenario of a fleet of city buses. The approach is demonstrated as a process consisting of two parts; Unsupervised modeling (where interesting features are discovered) and Guided search (where the previously found features are coupled to additional information sources). The modeling part searches for simple linear models in a group of vehicles, where interesting features are selected based on both non-randomness in relations and variability in the group. It is shown in an eight months long data collection study that this approach was able to discover features related to broken wheelspeed sensors. Strikingly, deviations in these features (for the vehicles with broken sensors) can be observed up to several months before a breakdown occur. This potentially allows for sufficient time to schedule the vehicle for maintenance and prepare the workshop with relevant components. © 2013 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2013
Keywords
Controller area network, Data collection, Diagnostic knowledge, Electronic control units, Information sources, Relevant components, Remote diagnostics, Self-organized models
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-23370 (URN)10.1109/ICMA.2013.6618034 (DOI)000335375900151 ()2-s2.0-84887900171 (Scopus ID)978-1-4673-5560-5 (ISBN)978-1-4673-5557-5 (ISBN)978-1-4673-5558-2 (ISBN)978-1-4673-5559-9 (ISBN)
Conference
10th IEEE International Conference on Mechatronics and Automation, IEEE ICMA 2013, Takamastu, Japan, 4-7 August, 2013
Available from: 2013-08-19 Created: 2013-08-19 Last updated: 2018-03-22Bibliographically approved
Prytz, R., Nowaczyk, S., Rögnvaldsson, T. & Byttner, S. (2013). Analysis of Truck Compressor Failures Based on Logged Vehicle Data. In: Hamid Reza Arabnia (Ed.), : . Paper presented at 9th International Conference on Data Mining, Las Vegas, Nevada, USA, July 22–25, 2013. CSREA Press
Open this publication in new window or tab >>Analysis of Truck Compressor Failures Based on Logged Vehicle Data
2013 (English)In: / [ed] Hamid Reza Arabnia, CSREA Press, 2013Conference paper, Published paper (Refereed)
Abstract [en]

In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread.

In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.

Place, publisher, year, edition, pages
CSREA Press, 2013
Keywords
Data Mining, Machine Learning, Fault Prediction, Automotive Diagnostics, Logged Vehicle Data
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-23457 (URN)1-60132-235-6 (ISBN)
Conference
9th International Conference on Data Mining, Las Vegas, Nevada, USA, July 22–25, 2013
Funder
VINNOVA
Available from: 2013-08-31 Created: 2013-08-31 Last updated: 2018-03-22Bibliographically approved
Nowaczyk, S., Prytz, R., Rögnvaldsson, T. & Byttner, S. (2013). Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data. In: Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani (Ed.), Twelfth Scandinavian Conference on Artificial Intelligence: . Paper presented at 12th Scandinavian Conference on Artificial Intelligence, Aalborg, Denmark, November 20–22, 2013 (pp. 205-214). Amsterdam: IOS Press
Open this publication in new window or tab >>Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Data
2013 (English)In: Twelfth Scandinavian Conference on Artificial Intelligence / [ed] Manfred Jaeger, Thomas Dyhre Nielsen, Paolo Viappiani, Amsterdam: IOS Press, 2013, p. 205-214Conference paper, Published paper (Refereed)
Abstract [en]

Predictive maintenance is becoming more and more important for the commercial vehicle manufactures, as focus shifts from product- to service-based operation. The idea is to provide a dynamic maintenance schedule, fulfilling specific needs of individual vehicles. Luckily, the same shift of focus, as well as technological advancements in the telecommunication area, make long-term data collection more widespread, delivering the necessary data.

We have found, however, that the standard attribute-value knowledge representation is not rich enough to capture important dependencies in this domain. Therefore, we are proposing a new rule induction algorithm, inspired by Michalski's classical AQ approach. Our method is aware that data concerning each vehicle consists of time-ordered sequences of readouts. When evaluating candidate rules, it takes into account the composite performance for each truck, instead of considering individual readouts in separation. This allows us more exibility, in particular in defining desired prediction horizon in a fuzzy, instead of crisp, manner. © 2013 The authors and IOS Press. All rights reserved.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2013
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389 ; 257
Keywords
Machine Learning, Relational Learning, AQ, Fault Prediction, Automotive Diagnostics, Logged Vehicle Data
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-24249 (URN)10.3233/978-1-61499-330-8-205 (DOI)000343477100022 ()2-s2.0-84894677920 (Scopus ID)978-1-61499-330-8 (ISBN)978-1-61499-329-2 (ISBN)
Conference
12th Scandinavian Conference on Artificial Intelligence, Aalborg, Denmark, November 20–22, 2013
Projects
ReDi2Service
Funder
VINNOVA
Available from: 2013-12-31 Created: 2013-12-31 Last updated: 2018-03-22Bibliographically approved
Nowaczyk, S., Byttner, S. & Prytz, R. (2012). Ideas for Fault Detection Using Relation Discovery. In: Lars Karlsson and Julien Bidot (Ed.), : . Paper presented at The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), 14–15 May 2012, Örebro, Sweden (pp. 1-6). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Ideas for Fault Detection Using Relation Discovery
2012 (English)In: / [ed] Lars Karlsson and Julien Bidot, Linköping: Linköping University Electronic Press, 2012, p. 1-6Conference paper, Oral presentation only (Refereed)
Abstract [en]

Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms.

Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions.

In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012
Series
Linköping Electronic Conference Proceedings, ISSN 1650-3740 ; 071
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-17718 (URN)
Conference
The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS), 14–15 May 2012, Örebro, Sweden
Projects
Redi2Service
Available from: 2012-05-25 Created: 2012-05-24 Last updated: 2018-03-22Bibliographically approved
Prytz, R., Nowaczyk, S. & Byttner, S. (2011). Towards relation discovery for diagnostics. In: Proceedings of the First International Workshop on Data Mining for Service and Maintenance: . Paper presented at 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 23-27). New York, NY: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Towards relation discovery for diagnostics
2011 (English)In: Proceedings of the First International Workshop on Data Mining for Service and Maintenance, New York, NY: Association for Computing Machinery (ACM), 2011, p. 23-27Conference paper, Published paper (Refereed)
Abstract [en]

It is difficult to implement predictive maintenance in the automotive industry as it looks today, since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of vehicle configurations and usage patterns. However, it is now becoming feasible to obtain and analyse on-board data on vehicles as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific vehicle configurations and usage. In order to be useful, though, such methods need to be able to detect interesting relations between a large number of available signals. This paper presents an unsupervised method for discovering useful relations between measured signals in a Volvo truck, both during normal operations and when a fault has occurred. The interesting relationships are found in a two-step procedure. In the first step, we identify a set of “good” models, by establishing an MSE threshold over the complete data set. In the second step, we estimate model parameters over time, in order to capture the dynamic behaviour of the system. We use two different approaches here, the LASSO method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.

Place, publisher, year, edition, pages
New York, NY: Association for Computing Machinery (ACM), 2011
Keywords
Fault detection, Machine learning, Vehicle diagnostics
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-15995 (URN)10.1145/2018673.2018678 (DOI)2-s2.0-80052625970 (Scopus ID)978-145030842-7 (ISBN)
Conference
17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Available from: 2011-09-07 Created: 2011-08-26 Last updated: 2018-03-22Bibliographically approved
Rögnvaldsson, T., Byttner, S., Prytz, R., Nowaczyk, S. & Svensson, M. Wisdom of Crowds for Intelligent Monitoring of Vehicle Fleets.
Open this publication in new window or tab >>Wisdom of Crowds for Intelligent Monitoring of Vehicle Fleets
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(English)Manuscript (preprint) (Other academic)
Abstract [en]

An approach is presented and experimentally demonstrated where consensus among distributed self-organized agents is used for intelligent monitoring of mobile cyberphysical systems (in this case vehicles). The demonstration is done on test data from a 30 month long field test with a city bus fleet under real operating conditions. The self-organized models operate on-board the systems, like embedded agents, communicate their states over a wireless communication link, and their states are compared off-line to find systems that deviate from the consensus. In this way is the group (the fleet) of systems used to detect errors that actually occur. This can be used to build up a knowledge base that can be accumulated over the life-time of the systems.

Keywords
Fault diagnosis, learning systems, mechatronics, self-monitoring, intelligent transportation systems
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-27970 (URN)
Funder
VINNOVASwedish Research Council
Available from: 2015-03-10 Created: 2015-03-10 Last updated: 2018-03-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8255-1276

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