Robust Driving Pattern Detection and Identification with a Wheel Loader Application

Information about wheel loader usage can be used in several ways to optimize customer adaption. First, optimizing the configuration and component sizing of a wheel loader to customer needs can lead to a significant improvement in e.g. fuel efficiency and cost. Second, relevant driving cycles to be used in the development of wheel loaders can be extracted from usage data. Third, online usage identification opens up for the possibility of implementing advanced look-ahead control strategies for wheel loader operation. The main objective of this paper is to develop an online algorithm that automatically, using production sensors only, can extract information about the usage of a machine. Two main challenges are that sensors are not located with respect to this task and that significant usage disturbances typically occur during operation. The proposed method is based on a combination of several individually simple techniques using signal processing, state automaton techniques, and parameter estimation algorithms. The approach is found to be robust when evaluated on measured data of wheel loaders loading gravel and shot rock.