Identifying Operator Usage of Wheel Loaders Utilizing Pattern Recognition Techniques
The information about how wheel loaders are used by costumers is not
well documented, but large quantities of sensor signal data are
recorded. This data makes the starting point to develop a robust
automatic pattern recognition algorithm to identify repetitive driving
operations.
The algorithm is composed by models of events that appear in
cycles. The cycles are pre-defined as short loading cycle and load and
carry. They consist of the phases; load, drive loaded, unload and
drive unloaded. In the load and carry cycle, the driving forward
loaded phase can last up to 400 meters. Beyond these two is something
called sub cycle defined. It contains cleaning cycle and reloading. A
cycle is identified by transition automata of events. To make the
identification more robust against disturbances, a probability
function is connected to the cycle identification.
The developed algorithm uses signals from sensors available in series
production wheel loaders. These signals are used to identify cyclic
behaviour in the shape of short loading cycle, load and carry and sub
cycle. After removal of completely standing still events, the
algorithm calculates the characteristic parameters for the identified
cycles and for their phases and present them in an excel
file. Validation showed that the algorithm can find 75% or more of
cyclic behaviour for bucket handling.
Karin Ohlsson-Öhman
2012
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