Feature-based sensor configuration and working-stage recognition of wheel loader

With the maturity of sensor and data acquisition technology, the intelligent development of multi-sensor integrated loader becomes inevitable. This paper focuses on the realization of intelligent recognition of loader's working stage via low-cost and efficient sensor configuration of bulk operation data. A feature selection method, Redundancy-Complementariness Dispersion-and-Relevance-based (RCDR) is introduced to select the optimal configuration with fewer sensors. By comparing different combinations of window size and various classifiers, it is found that the sensor set configured based on RCDR feature selection can achieve an accuracy of 94.17% in working-stages recognition. Arguably, the method is potent in configuring a subset of sensors with fewer sensors and accurately recognizing working stages in various types of low-cost operation data without introducing an intelligent calibration system (IF-Then strategy). Future research is expected to tackle the limited applicability of the model caused by data discontinuity, window size combination difference and the change of loader type.