Study on Skill Analysis in a Hydraulic Excavator using Convolutional Autoencoder

Abstract

In the construction industry, it is desirable to develop a system to easily judge skills of workers in order to effectively pass on the skills of skilled workers to unskilled workers. This report proposes a skill analysis method in hydraulic excavator operators using a convolutional autoencoder (CAE) that is capable of nonlinear mapping to low dimensionally space. CAE is trained with the operation data of a skilled operator to acquire characteristics of the skilled operator. Then, the operation data of an unskilled operator is input to the trained CAE to analyze the unskilled operator's skill. CAE detects operations of the unskilled operator containing features that differ from the operation of the skilled operator out of many operations. First, it is confirmed that CAE can save information of the operation data in a low dimensional space than principal component analysis that is a linear mapping for dimensionality reduction. Next, the result of the proposed method for the unskilled operator is shown. Effectiveness of the result is validated by comparing a few operation data of both operators detected by the proposed method.