Cycle Condition Identification of Loader Based on Optimized KNN Algorithm

Abstract

The working conditions of loaders alternate between stages of full or empty loads, loading or unloading, and moving forward or backward, which complicates the vehicle's characteristic response. Based on the K-nearest neighbor (KNN) algorithm and a principal component analysis (PCA) method, stages recognition algorithm under the V-type working conditions of a loader was studied. First, the collected transmission signals were noise-reduced and filtered. Second, the PCA was used to reduce the dimensions of the data. Finally, the working condition samples were established from the data obtained, which were later trained and classified using the KNN algorithm. Compared with the neural network algorithm, the accuracy of the optimized KNN algorithm reaches 99.4%, and its running time is 2.1s. The algorithm described in this paper guarantees a high accuracy under recognition of the loader conditions in a short running time. It can be extended to an intelligent identification using big data and artificial intelligence control of the construction machinery. INDEX TERMS K-nearest neighbor (KNN), loader, principal component analysis (PCA), condition identification.

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