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Automated Usage Characterization of Mining Vehicles For Life Time Prediction
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering. Epiroc Rock Drills AB, Sweden.ORCID iD: 0000-0001-9493-7256
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7349-1937
Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4965-1077
Epiroc Rock Drills AB, Sweden.
2020 (English)In: IFAC PAPERSONLINE, ELSEVIER , 2020, Vol. 53, no 2, p. 11950-11955Conference paper, Published paper (Refereed)
Abstract [en]

The life of a vehicle is heavily influenced by how it is used, and usage information is critical to predict the future condition of the machine. In this work we present a method to categorize what task an earthmoving vehicle is performing, based on a data driven model and a single standalone accelerometer. By training a convolutional neural network using a couple of weeks of labeled data, we show that a three axis accelerometer is sufficient to correctly classify between 5 different classes with an accuracy over 96% for a balanced dataset with no manual feature generation. The results are also compared against some other machine learning techniques, showing that the convolutional neural network has the highest performance, although other techniques are not far behind. An important conclusion is that methods and ideas from the area of Human Activity Recognition (HAR) are applicable also for vehicles. Copyright (C) 2020 The Authors.

Place, publisher, year, edition, pages
ELSEVIER , 2020. Vol. 53, no 2, p. 11950-11955
Keywords [en]
Maintenance scheduling and production planning; Neural networks in process control; Measurement and instrumentation
National Category
Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:liu:diva-176894DOI: 10.1016/j.ifacol.2020.12.719ISI: 000652593100507OAI: oai:DiVA.org:liu-176894DiVA, id: diva2:1572002
Conference
21st IFAC World Congress on Automatic Control - Meeting Societal Challenges, ELECTR NETWORK, jul 11-17, 2020
Note

Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP)

Available from: 2021-06-23 Created: 2021-06-23 Last updated: 2022-04-20
In thesis
1. Condition Monitoring in Mobile Mining Machinery
Open this publication in new window or tab >>Condition Monitoring in Mobile Mining Machinery
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The global mining industry is currently facing a huge transition from manually operated individual vehicles, to autonomous vehicles being part of an industrial process-like environment. The change is driven by the never ending need for efficient, safe, and environmentally friendly operations. One intentional consequence is an increased distance between the operator, and the machine being operated. This enables safer working environments and reduced cost for ventilation and other supporting systems in a mine, but it also results in the loss of the systems most important sensor. The transition from manual to autonomous operation requires this gap to be filled from a system awareness perspective, which lately has become evident with the large resources that car manufacturers use to develop self-driving cars. This thesis also targets system awareness, but of the internal kind. By this we mean knowing the condition of the machine and its capabilities. The operator is the most important sensor also for internal condition, and if no operator is present on the machine, this gap needs to be filled.

The mining industry is categorized by small series and significant customization of machinery. This is a direct result of the geological prerequisites, where differently shaped ore bodies cause large differences in mine layout and mining methods. This thesis explores how methods estimating the health of mining vehicles can be used in this setting, by utilizing sensor signals to make assessments of the current vehicle condition and tasks.

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.

Two applications are studied. Mine trucks have slow degradation modes, such as crack propagation and fatigue, that are difficult to handle with data driven approaches since data collection requires significant amounts of time. A contribution in this thesis, is a method to utilize short term measurement data together with data driven methods to obtain the loads of a vehicle, and then to use physics based approaches to estimate the actual damage.

The second application considers monitoring faults in hydraulic rock drills using online measurements during operation. The rock drill is a specifically difficult case, since severe vibration levels limits the locations and types of sensors that can be used. The main contribution is a method to handle individual differences when classifying internal faults using a single pressure sensor on the hydraulic supply line. A complicating factor is the large influence of wave propagation, causing different individuals to show different behavior.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 50
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2225
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-184433 (URN)10.3384/9789179292997 (DOI)9789179292980 (ISBN)9789179292997 (ISBN)
Public defence
2022-06-03, Ada Lovelace, B Building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2022-04-20 Created: 2022-04-20 Last updated: 2022-04-21Bibliographically approved

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