基于改进超限学习机的制造过程质量监控模型

Manufacturing Process Quality Monitoring Method Based on Modified ELM Algorithm

针对超限学习机识别模型在制造过程质量异常模式识别中存在输入权值和偏置向量随机设置导致识别效率低的问题,通过粒子群优化算法对超限学习机模型的网络结构进行优化,提出一种基于改进超限学习机的制造过程质量监控模型。利用主成分分析方法进行过程质量数据的特征提取,利用主成分特征对识别模型进行训练,利用粒子群优化算法对识别模型的网络结构进行优化。仿真实验和实测数据均表明:所提基于改进超限学习机的制造过程质量异常识别模型的识别效率明显高于其他同类模型,能够用于制造过程的实时监控。

Aiming at the problem of low recognition efficiency in the extreme learning machine(ELM)recognition model caused by the random setting of input weights and bias vectors in the quality abnormal pattern recognition of the manufacturing process,the network structure of the ELM recognition model was optimized by using particle swarm optimization algorithm,a manufacturing process quality monitoring model was proposed based on improved ELM.Feature extraction of process quality data was carried out by using principal component analysis.The recognition model was trained by using principal component features.Particle swarm optimization algorithm was used to optimize the network structure of the recognition model.The simulation experiments and measured data indicate that the recognition efficiency of the proposed abnormal identification model in the manufacturing process based on the improved ELM is significantly higher than other similar models,this improved model can be used for real-time monitoring of the manufacturing process.