基于模糊神经网络的矿井提升机制动系统故障诊断方法

Fault Diagnosis Method of Mine Hoist Braking System Based on Fuzzy Neural Network

煤矿提升是采煤过程中至关重要的环节,负责着提升或下放人员与矸石等的重要工作,牵动着整个开采过程的命脉。提升机制动系统是保证矿井提升机安全运行的最后一道屏障,它的安全可靠至关重要,因此对其进行故障诊断具有重要意义。以单绳缠绕式2JTP-1.2×1.0型提升机为研究对象,利用模糊神经网络对其进行了故障诊断研究。首先,对制动系统的结构进行分析明确了故障类型,然后将其故障样本代入模糊神经网络进行网络的训练,即将监测系统采集到的数据代入隶属度函数进行模糊化,再将它的输出结果作为神经网络的输入,进行网络的训练,得到诊断结果;最后,对训练好的模糊神经网络进行了实验验证,结果表明,模糊神经网络能较为准确地对故障做出诊断。

Coal mine hoisting is a crucial link in the coal mining process,which is responsible for the important work of elevating or delegating personnel and gangue,affecting the lifeline of the whole mining process.The dynamic system of hoisting mechanism is the last barrier to ensure the safe operation of mine hoist.Its safety and reliability are very important.Therefore,fault diagnosis is of great significance.The single-rope twine type 2 JTP-1.2×1.0 hoist is taken as the research object,and the fault diagnosis is studied by using fuzzy neural network.First,the structure of the braking system was analyzed to determine the fault type,and then the fault samples were substituted into the fuzzy neural network for network training.The data collected by the monitoring system was substituted into the membership function for fuzzy,and then the output results were taken as the input of the neural network for network training and diagnosis.Finally,the experimental results show that the fuzzy neural network can diagnose faults accurately.