基于神经网络算法的FISHER智能阀门定位器故障诊断系统

FISHER Intelligent Valve Positioner Fault Diagnosis System Based on Neural Network Algorithm

在工业自动化领域,智能阀门定位器在长期运行中不可避免地会遇到各种故障,若不能及时诊断和处理这些故障,将严重影响生产过程的稳定性和安全性。神经网络算法具有出色的非线性映射能力和学习能力,能为智能阀门定位器的故障诊断提供有效的技术支持。该算法能够通过模仿人脑的处理机制,从复杂、高维、非线性的数据中学习故障的特征和规律,从而准确诊断阀门定位器故障。介绍了基于神经网络算法的FISHER智能阀门定位器故障诊断系统的研究与实现过程。从神经网络算法的原理、网络结构入手,阐述了系统的总体设计思路和关键技术。

In the field of industrial automation,intelligent valve positioners inevitably encounter various faults during long-term operation.If these faults cannot be diagnosed and dealt with in a timely manner,they will seriously affect the stability and safety of the production process.Neural network algorithms have excellent nonlinear mapping and learning abilities,which can provide effective technical support for fault diagnosis of intelligent valve positioners.This algorithm can learn the characteristics and patterns of faults from complex,high-dimensional,and nonlinear data by imitating the processing mechanism of the human brain,thereby achieving accurate diagnosis of valve locator faults.This paper introduces the research and implementation process of a FISHER intelligent valve positioner fault diagnosis system based on neural network algorithms.Starting from the principle and network structure of neural network algorithms,the overall design concept and key technologies of the system are elaborated.