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.

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.