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Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. Atlas Copco Rock Drills AB, Örebro, Sweden.ORCID iD: 0000-0001-9493-7256
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Atlas Copco Rock Drills AB, Örebro, Sweden.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4965-1077
2017 (English)In: PHM 2017. Proceedings of the Annual Conference of the Prognostics and Health Management Society 2017, St. Petersburg, Florida, USA, October 2–5, 2017 / [ed] Anibal Bregon and Matthew J. Daigle, phmSociety , 2017, p. 98-107Conference paper, Published paper (Refereed)
Abstract [en]

The life and condition of a MT65 mine truck frame is to a large extent related to how the machine is used. Damage from different stress cycles in the frame are accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors used. To make a monitoring system cheap and robust enough for a mining application, a small number of robust sensors are preferred rather than a multitude of local sensors such as strain gauges. The main question to be answered is whether a low number of robust on-board sensors can give the required information to recreate stress signals at various locations of the frame. Also the choice of sensors among many different locations and kinds are considered. A final question is whether the data could also be used to estimate road condition. By using accelerometer, gyroscope and strain gauge data from field tests of an Atlas Copco MT65 mine truck, coherence and Lasso-regression were evaluated as means to select which signals to use. ARX-models for stress estimation were created using the same data. By simulating stress signals using the models, rain flow counting and damage accumulation calculations were performed. The results showed that a low number of on-board sensors like accelerometers and gyroscopes could give enough information to recreate some of the stress signals measured. Together with a linear model, the estimated stress was accurate enough to evaluate the accumulated fatigue damage in a mining truck. The accumulated damage was also used to estimate the condition of the road on which the truck was traveling. To make a useful road monitoring system some more work is required, in particular regarding how vehicle speed influences damage accumulation.

Place, publisher, year, edition, pages
phmSociety , 2017. p. 98-107
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152214ISBN: 9781936263264 (electronic)OAI: oai:DiVA.org:liu-152214DiVA, id: diva2:1259820
Conference
annual conference of the prognostics and health management society 2017, PHM17, October 2-5, St. Petersburg, Florida, USA
Funder
Wallenberg FoundationsAvailable from: 2018-10-31 Created: 2018-10-31 Last updated: 2019-11-20Bibliographically 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: 2019-11-25Bibliographically approved

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Jakobsson, ErikFrisk, ErikKrysander, Mattias

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