摘要
本文针对机电一体化设备故障预测问题,深入探究了基于灰色系统理论的精度优化方法,详细阐述了从设备故障特征提取到基于灰色系统理论的故障预测模型构建与优化全过程,包括残差修正GM(1,1)模型、引入粒子群算法以及参数优化的应用等。实验验证表明,优化后的模型能够显著提高故障预测精度,有效减少预测误差,为机电一体化设备的维护与管理提供强有力的技术支撑,对提升工业生产系统的可靠性和稳定性具有极为重要的意义。
This article focuses on the problem of fault prediction for mechatronics equipment and explores in depth the accuracy optimization method based on grey system theory.It elaborates on the entire process from equipment fault feature extraction to the construction and optimization of fault prediction models based on grey system theory,including residual correction GM(1,1)model,introduction of particle swarm optimization algorithm,and application of parameter optimization.Experimental verification shows that the optimized model can significantly improve the accuracy of fault prediction,effectively reduce prediction errors,and provide strong technical support for the maintenance and management of mechatronics equipment,which is of great significance for improving the reliability and stability of industrial production systems.
作者
王延英
WANG Yanying(Jinan Engineering Vocational and Technical College,Ji’nan 250200,China)
出处
《数字通信世界》
2025年第7期27-29,共3页
Digital Communication World
关键词
灰色系统理论
机电一体化
设备故障
预测精度优化
grey system theory
mechatronics integration
equipment malfunction
optimization of prediction accuracy
