Multivariate principal component analysis for production and energy consumption of cutter suction dredger

摘要

Cutter suction dredgers perform a major part in the field of dredging engineering in harbors, fairways, and land reclamation. However, there are many parameters in cutter suction dredger operation so that it is difficult to guarantee the stability of production. In consideration of the issue of enormous parameters in dredging operation, mathematical dimensional reduction method which uses multivariate primary component analysis is proposed. The method can calculate the contribution rate and cumulative contribution rate of each parameter and then select the principal components which influents the production and energy consumption. These parameters represent the majority of the original data information, while not interrelated with each other. The primary components can be used to guide the regulation and control of the parameters, reduce regulatory parameters and operational complexity and provide a theoretical basis for intelligent automation of dredging operations.