实验室计量设备检定误差智能预测方法研究

Research on Intelligent Prediction Method of Calibration Error of Laboratory Measuring Equipment

为了保证实验室测量数据的真实性,减少计量设备检定误差,提出实验室计量设备检定误差智能预测方法。利用工控机、相控阵换能器等设计信息采集系统,采用模糊聚类算法确定聚类中心,设置隶属度矩阵对采集到的数据做聚类处理。结合设备状态数据建立实验室计量设备状态评估模型。结合设备状态评估结果,利用构建长短期记忆网络搭建实验室计量设备检定误差智能预测模型,利用粒子群算法对模型求解,得到预测结果。实验结果表明,所提方法的预测结果和实际误差相符,适用于实验室中的计量设备检定误差预测,实际应用效果好。

In order to ensure the authenticity of laboratory measurement data and reduce the calibration error of measuring equipment,an intelligent prediction method of calibration error of laboratory measuring equipment is proposed.The information acquisition system is designed by using industrial computer,phased array transducer,etc.The clustering center is determined by using fuzzy clustering algorithm,and the membership matrix is set to cluster the collected data.Based on the equipment status data,the state evaluation model of laboratory measuring equipment is established.Combined with the evaluation results of equipment status,the intelligent prediction model of calibration error of laboratory measuring equipment is built by using the long-term and short-term memory networks.The model is solved by using particle swarm optimization algorithm,and the prediction results are obtained.The experimental results show that the prediction results of the proposed method are consistent with the actual errors.It is suitable for the prediction of the calibration errors of the measuring equipment in the laboratory,and the practical application effect is good.