T-S模糊神经网络在风机故障诊断中的应用  

Application of T-S fuzzy neural network in fault diagnosis of ventilator

作　　者：张昭[1] 李风雷[1] 田琦[1] 

机构地区：[1]太原理工大学环境科学与工程学院,山西太原030024

出　　处：《燕山大学学报》2016年第4期336-341,共6页Journal of Yanshan University

基　　金：国家"十二五"科技支撑计划项目(2012BAJ04B02)

摘　　要：针对供热锅炉房风机故障的不确定性和复杂性的特点,结合神经网络的自适应自学习能力和模糊系统利用语言描述来获取知识的优点,建立了一种基于T-S模糊神经网络的风机二级故障诊断模型,以风机常见故障振动信号的特征频谱峰值变化为依据,诊断故障类型,并对故障原因进行识别。应用MATLAB软件进行系统仿真测试,通过实例与BP神经网络进行了对比分析,结果表明基于模糊神经网络的故障诊断方法收敛速度快、诊断精度高、效果较稳定,能够有效地诊断风机故障。Aiming at the ventilator faults with uncertainty and complexity characteristics in heating boiler house, combining the a- daptive and learning capabilities of neural network and the language description to get knowledge of fuzzy system, a two stage fault diagnosis model of ventilator based on T-S fuzzy neural network is proposed, which can diagnose the types of faults and identify the causes of faults, according to the changes in the characteristics spectrum values of the vibration signal of common faults.With the simulation tests by MATLAB software, through the example comparison of the fuzzy neural network and the BP neural network, the results illustrate that the fault diagnosis method of the fuzzy neural network can recognize the faults rapidly, accurately and steadily, which provides a efficient way for the diagnosis.

关 键 词：风机 模糊神经网络 BP神经网络 故障诊断 

分 类 号：TU995[建筑科学—供热、供燃气、通风及空调工程] TP277[建筑科学—市政工程]