基于功率信息的机床设备综合效率智能识别方法

机床的设备综合效率是衡量机床运行状况的关键指标，受到制造企业的普遍关注，但传统的识别方法在获取时间稼动率、性能稼动率和产品合格率指标时存在难度大、成本高、普适性差的问题。为此，提出了一种基于功率信息的设备综合效率智能识别方法，获取机床功率信息时频域特征，构建采样周期特征向量，并采用主成分分析法构建状态匹配库，结合最近邻算法识别运行状态，量化运行状态持续时间，计算时间稼动率；同时借助滑动移窗构建加工周期特征向量，采用距离匹配获取实际加工件数，结合MES系统计算性能稼动率与产品合格率。最后，以铣削加工为例，其设备综合效率理论值与实际值相对误差为4.99%，验证了该方法的可行性与实用性。

An intelligent identification approach of overall equipment effectiveness for machine tool based on power information

Overall equipment effectiveness (OEE) is a key indicator to measure the operation status of machine tool, attracting much attention from manufacturing enterprises. However, the conventional approaches have the disadvantages of inconvenience, high cost and poor universality in calculating the availability, performance and quality rate indicators of OEE. Hence, an intelligent identification approach of OEE based on power information was proposed. Firstly, the time-frequency characteristics of machine tool power information were obtained, and the sampling period feature vector was established. Then, the principal component analysis was employed to construct the status matching library. Combined with the nearest neighbor algorithm, the running status was identified, and its duration was quantified to calculate the availability. In addition, the sliding window was applied to develop the processing period feature vector, and the distance matching was used to obtain the actual number of processing pieces. Combining the number of processing pieces with the data obtained by the MES system, the performance and quality rate were calculated. To verify the feasibility and practicability of the approach, the experimental study of the milling was performed, and the relative error between the theoretical value and the actual value is 4.99%.