基于NSET的鼓风设备故障预警方法

Blasting Equipment Fault Warning Method Based on Nonlinear State Estimate Technology

鼓风设备运行过程中的参数信号多种多样,其中包含温度、压力、流量、振动等,在机器运行当中存在许多影响安全和稳定运行的因素,一旦发生设备故障将会对企业带来不小的经济损失,所以工业设备的故障预警起了至关重要的作用。论文总结了非线性状态估计技术建模方法的应用原理,在非线性状态估计技术的基础上进行了优化和改进,引入聚类分析的理念,利用马氏距离的思想,对过程记忆矩阵进行优化,提出了一种基于标准欧氏距离的改进非线性状态估计技术的建模方法,能够应用于多种工业设备异常的预警。通过大量实验进行测试,实验数据表明,将该方法与传统方法比较,能够有效地减少冗余数据,提高了诊断效率。

There are various parameters in the operation of the blast equipment,including temperature,pressure,flow,vibration,etc.There are many factors affecting safety and stable operation during the operation of the machine.Once the equipment failure occurs,it will bring considerable economic losses to the enterprise.Thus the early warning of industrial equipment failure has played a crucial role.This paper summarizes the application principle of nonlinear state estimation technology modeling method,optimizes and improves the principle on the basis of nonlinear state estimation technology,introduces the idea of cluster analysis,and uses the idea of Mahalanobis distance to optimize the process memory matrix.A modeling method based on standard Euclidean distance improved nonlinear state estimation technique is proposed,which can be applied to the early warning of various industrial equipment anomalies,and an example is verified.Compared with the traditional method without improvement,this method can effectively reduce redundant data and improve diagnostic efficiency.