A hierarchical fuzzy inference method for skill evaluation of machine operators

In machine work, the productivity, energy efficiency, and the quality of the work depend strongly on the skills of the human operator. This paper proposes a hierarchical method for skill evaluation of human operators in machine work during their normal work. The method refines skill metrics obtained from work cycle recognition-based evaluation system proposed earlier by the authors. The proposed skill components are: machine controlling skills, control parameter tuning skills, knowledge of the work technique and strategy, and planning and decision making skills. The skill components in each task are evaluated by a dedicated fuzzy inference system, whose rule base is generated automatically. The method is utilized to evaluate skills of nine operators of a cut-to-length forest harvester.