Application of AHP and EIE in Reliability Analysis of Complex Production Lines Systems

Abstract

It is necessary to grasp the operation state of the production system for scientific scheduling, process improvement, fault analysis, equipment maintenance, or replacement. The matter-element information entropy is proposed to evaluate the health index of the product line, and the parameter self-optimization support vector machine is used to predict the future health index. A new type of three-dimensional cross compound element is established by synthesizing the operation state of equipment, energy consumption, production efficiency, and human factors. The subjective, objective, and joint weights are determined by the analytic hierarchy process (AHP) method, entropy, and the combination weighting method, respectively. The health index is calculated by complex element correlation entropy. The calculations of the beer filling production line show that the combined weighting method is an effective method on the health index calculation and can accurately reflect the actual operation state of the production. Support vector machine (SVM) optimized by multiparameters is established to predict the health index; the simulation shows that Least Squares Support Vector Machine (LSSVM) based on radial basis function (RBF) has prominent prediction effect. It can provide accurate data support for the production and management of enterprises.