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A system for underground road condition monitoring
KTH Royal Institute of Technology, Stockholm; ABB Corporate Research, Västerås.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. Epiroc, Örebro.ORCID iD: 0000-0001-9493-7256
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-8298-3933
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Boliden Mines, Boliden, Sweden.
2020 (English)In: International Journal of Mining Science and Technology, ISSN 2095-2686Article in journal (Refereed) In press
Abstract [en]

Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of WiFi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm. The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances. The system is demonstrated on experimental data collected in a Swedish underground mine.

Place, publisher, year, edition, pages
Elsevier, 2020.
Keywords [en]
Localization, Road condition monitoring, Scheduling, Underground mining
National Category
Other Engineering and Technologies not elsewhere specified
Identifiers
URN: urn:nbn:se:liu:diva-165752DOI: 10.1016/j.ijmst.2020.04.006Scopus ID: 2-s2.0-85083825323OAI: oai:DiVA.org:liu-165752DiVA, id: diva2:1431141
Available from: 2020-05-19 Created: 2020-05-19 Last updated: 2020-05-25Bibliographically approved
In thesis
1. Data-driven Condition Monitoring in Mining Vehicles
Open this publication in new window or tab >>Data-driven Condition Monitoring in Mining Vehicles
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Situation awareness is a crucial capability of any autonomous system, including mining vehicles such as drill rigs and mine trucks. Typically situation awareness is interpreted as the capability of an autonomous system to interpret its surroundings and the intentions of other agents. The internal system awareness however, is often not receiving the same focus, even though the success of any given mission is completely dependent of the condition of the agents themselves. The internal system awareness in the form of vehicle health is the focus of this thesis.

As the mining industry becomes increasingly automated, and vehicles become increasingly advanced, the need for condition monitoring and prognostics will continue to rise. This thesis explores data-driven methods that estimate the health of mining vehicles to accommodate those needs. We do so by utilizing available sensor signals, common on a large amount of mining vehicles, to make assessments of the current vehicle condition and tasks. The mining industry is characterized by small series of highly specialized vehicles, which affects the possibility to use more traditional prognostic solutions.

The resulting health information can be used both to aid in tasks such as maintenance planning, but also as an important input to decision making for the planning system, i.e. how to run the vehicle for minimum wear and damage, while maintaining other mission objectives.

The contributions include: a) A method to use operational data to estimate damage on the frame of a mine truck. This is done using system identification to find a model describing stresses in the structure with input from other sensors such as accelerometers, load sensors and pressure sensors. The estimated stress time signal is in turn used to calculate accumulated damage, and is shown to reveal interesting conclusions on driver behavior. b) A method to characterize the different driving tasks by using an accelerometer and a convolutional neural network. We show that the model is capable of classifying the vehicle task correctly in 96 % of the cases. And finally c), a system for underground road monitoring, where a quarter car model and a Kalman filter are used to generate an estimate of the road profile, while positioning the vehicle using inertial measurements and access point signal strength.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 22
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1856
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-162132 (URN)10.3384/lic-diva-162132 (DOI)9789179299729 (ISBN)
Presentation
2019-12-16, Ada Lovelace, B-huset, Campus Valla, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Note

Ytterligare forskningsfinansiär: Epiroc Rock Drills AB

Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2020-05-19Bibliographically approved

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Jakobsson, ErikLindfors, Martin

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