Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors
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.
Erik Jakobsson, Erik Frisk, Robert Pettersson and Mattias Krysander
2017
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