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.
Highlights
In recent years, the organizational reliability analysis of the production system, the human reliability analysis, and the system reliability analysis under the dynamic environment have become one of the most concerned hot issues in the industrial engineering and other scientific circles [1].Health index is one of the main indexes to measure the reliability of the system
Aiming at the problem of health index evaluation and prediction of complex production lines, a new data mining method based on extension matter-element entropy and support vector machine is designed
analytic hierarchy process (AHP) and matter-element information entropy are used to determine subjective and objective weights; the combination weighting method is used to calculate joint weights, which improves the reliability of information entropy weights
Summary
Health index is one of the main indexes to measure the reliability of the system. Health assessment refers to relying on advanced testing methods, combining reliable and effective assessment methods using complete operation data to analyze, predict, and judge; it can effectively improve the system maintenance support capability and reduce maintenance costs and save spare parts [2, 3]. The research of its evaluation method is mainly concentrated on two directions: one is system modeling and analysis. The modeling of the system is difficult and the adaptability of the method is not strong. The method is flexible and adaptable, with the maturity of machine learning and statistical analysis methods; data-driven method becomes the mainstream algorithm for system health assessment [4, 5]
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