Determination of the minimum sample size for the transmission load of a wheel loader based on multi-criteria decision-making technology

Abstract The present paper aims to provide a new approach in estimating the minimum sample size of the transmission load of a wheel loader under multiple operating conditions based on multi-criteria decision-making (MCMD) technology. Extreme load values (ELVs) and load cycles under multi-operating conditions are carefully considered, and the mean and the standard deviation of ELVs and the fatigue life are the three criteria selected for estimating the sample size. Using MCMD, the weight values of the three criteria are determined, where the eigenvector and entropy information methods, together with linear combination weighting, are adopted. The optimal minimum sample size (MSS) is estimated based on the feasible values determined by the three criteria and their corresponding weight values. As an example, the load time history of the semi-axle of a wheel loader is analyzed in detail. As the ELVs and load cycles are studied, the optimal MSS can properly represent the load characteristics. The objectivity and validity of the optimal MSS are assured using the combination of the eigenvector and entropy information methods.