Offline driving pattern detection and identification under usage disturbances
Optimizing the configuration of a wheel loader to customer needs can
lead to a significant increase in efficiency with respect to fuel
economy, cost, component dimensioning etc. Experience show that even
modest customer adaptation can save around 20\% of fuel cost. A key
motivator for this work is that wheel loader manufacturers in
general does not have full information about customer usage of the
machine and the main objective here is to develop an algorithm that
automatically, using only production sensors, extracts information
about the usage of a machine at a specific customer site. Two main
challenges are that sensors are not located with respect to this
task and the significant usage disturbances that typically occur
during operation. The proposed solution is a robust method, based on
a mix of techniques using basic signal processing, state automaton
techniques, and parameter estimation algorithms. A key property of
the method is the method of combining, individually very simple,
basic techniques in a scheme where robustness are introduced. The
approach is evaluated on measured data of a wheel loader loading
gravel and shot rock.
Tomas Nilsson, Christofer Sundström, Peter Nyberg, Erik Frisk and Mattias Krysander
2012
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