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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. Epiroc Rock Drills AB.ORCID iD: 0000-0001-9493-7256
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Epiroc Rock Drills AB.
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Department of Electrical Engineering, Vehicular Systems.
2017 (English)In: Proceedings of annual conference of the prognostics and health management society 2017, PHM17, 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
2017. p. 98-107
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-152214ISBN: 978-1-936263-26-4 (electronic)OAI: oai:DiVA.org:liu-152214DiVA, id: diva2:1259820
Conference
annual conference of the prognostics and health management society 2017, PHM17
Funder
Wallenberg FoundationsAvailable from: 2018-10-31 Created: 2018-10-31 Last updated: 2018-10-31

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https://www.phmsociety.org/events/conference/phm/17/proceedings

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Frisk, ErikKrysander, Mattias

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