基于SOM-Kmeans的机床能效等级评价方法

机床能效等级评价是提高能源效率的基础。现有机床能效评价方法多运用能量利用率和比能等指标评价特定加工过程能效，未考虑加工能力对机床能效评价的影响，致使无法综合评价机床能效等级。为此，从机床加工能力和能耗特性角度构建机床能效等级评价指标，提出基于自组织映射神经网络-K均值(SOM-Kmeans)的机床能效等级评价方法。首先，分析机床加工能力特性和时段能耗特性，构建机床加工能力指标和多时段能耗特性指标；其次，运用SOM-Kmeans对机床能效信息进行聚类分析，并采用主成分分析法(PCA)对机床能效等级进行综合评价；最后，通过实际案例验证了所提方法的有效性和实用性。

SOM-Kmeans Based Machine Tools Energy Efficiency Grade Evaluation

The evaluation of machine tool energy efficiency grade is the basis for improving energy efficiency. Most of the existing evaluation methods mostly use energy utilization and specific energy to evaluate the energy efficiency of specific machining processes, without considering the influence of machining capacity on machine tool energy efficiency evaluation. To comprehensively evaluate machine tools energy efficiency grade, the machine tools energy efficiency grade evaluation method based on Self-organizing Map and Kmeans (SOM-Kmeans) is proposed in terms of machining capacity and energy consumption characteristics. Firstly, the energy consumption and machining capacity characteristics are analyzed, and then the multi-period energy consumption characteristics and machining capacity index are constructed. Secondly, the cluster analysis of machine tool energy efficiency information is carried out using SOM-Kmeans and the machine tool energy efficiency grade is obtained based on principal component analysis (PCA). Finally, the effectiveness and practicability of the proposed method are verified by actual cases.