嵌入式系统在液压状态监测与智能故障诊断中的应用

针对国产地F元轨采矿设备液压系统故障率高，而其检测和故障诊断手段落后的背景，提出了一种基于嵌入式系统的液搓状态监测与智能故障诊断模型，陔故障诊断模型通过没置在液压系统中的多源异质传感器实时采集设备特征信息，并采用模糊神经网络来实现数据的融合处理，诊断结果通过界而友好的LCD显示，实现了故障状态的实时岭测和故障的智能诊断。通过该故障诊断模型，有效地解决了无轨采矿没备液搓系统故障率高而其检测和故障诊断手段落后的问题。提高了设备故障诊断的自动化和错能化．增强了设备的市场竞争力。

Application of Hydraulic State Monitoring and Intelligent Fault Diagnosis Based on Embedded System

For the high fault rate of homemade underground trackless mining equipment's hydraulic system and its backward monitoring and diagnosis methods,a kind of state monitoring and intelligent fault diagnosis model of hydraulic system based on embedded system has been presented.This model utilizes multi-source,heterologous sensors to acquire all kinds of characteristic information of hydraulic system and utilizes fuzzy neural network as executive institution of data fusion,and the fault diagnosis result is displayed by LCD,furthermore to accurately complete state monitoring and the intelligent fault diagnosis of hydraulic system.Through this fault diagnosis model,the problems of high fault rate of underground trackless mining equipment's hydraulic system and its backward monitoring and diagnosis methods have been solved efficiently,furthermore to improve the automation and intelligent fault diagnosis of underground trackless mining equipment and enhance its market competitive ability.