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Fatigue Damage Monitoring for Mining Vehicles using Data Driven Models
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. Epiroc Rock Drills AB, Örebro, Sweden.ORCID iD: 0000-0001-9493-7256
Epiroc Rock Drills AB, Örebro, Sweden.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4965-1077
2020 (English)In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 11, no 1, article id 004Article in journal (Refereed) Published
Abstract [en]

The life and condition of a mine truck frame are related to how the machine is used. Damage from stress cycles is 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, especially in a harsh mining application. To make a monitoring system cheap and robust, sensors already available on the vehicles are preferred rather than additional strain gauges. The main question in this work is whether the existing on-board sensors can give the required information to estimate stress signals and calculate accumulated damage of the frame. Model complexity requirements and sensors selection are also considered. A final question is whether the accumulated damage can be used for prognostics and to increase reliability. The investigation is performed using a large data set from two vehicles operating in real mine applications. Coherence analysis, ARX-models, and rain flow counting are techniques used. The results show that a low number of available on-board sensors like load cells, damper cylinder positions, and angle transducers can give enough information to recreate some of the stress signals measured. The models are also used to show significant differences in usage by different operators, and its effect on the accumulated damage.

Place, publisher, year, edition, pages
Rochester, NY, United States: Prognostics and Health Management Society , 2020. Vol. 11, no 1, article id 004
Keywords [en]
Fatigue damage, System identification, Damage accumulation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-165753OAI: oai:DiVA.org:liu-165753DiVA, id: diva2:1431155
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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