基于遗传算法和BP神经网络的多联机阀类故障诊断

Valve Fault Diagnosis of Variable Refrigerant Flow System based on Genetic Algorithm and Back Propagation Neural Network

针对多联机系统(变制冷剂流量系统)阀类故障的诊断特征变量冗杂、诊断效率低的问题,提出一种复合诊断模型,利用遗传算法在原始特征集中搜索特征子集,与参数优化后的BP神经网络模型结合,对多联机阀类故障进行检测和诊断。本文从原始特征集中优化选择了带有18个特征变量的最优特征子集,用该模型对电子膨胀阀卡死、电子膨胀阀泄漏和四通阀故障3种故障进行检测,结果表明:该复合诊断模型对故障检测率提高,其中电子膨胀阀的卡死故障检测率提升8%,整体诊断正确率提高到99.27%;该复合诊断模型大大提高了诊断效率,使测试时间缩短了52.17%,表明该复合诊断模型具有较好的故障诊断效果。

Variable refrigerant flow(VRF)valve fault detection and diagnosis usually face the problems of too many features and low efficiency.Therefore,a high-efficiency hybrid model based on a genetic algorithm(GA)and back propagation neural network(BPNN)was proposed.In this hybrid model,the feature subset is extracted from the original feature set of the VRF using the GA,and then the parameter-optimized neural network is used to detect and diagnose VRF valve faults.In this study,the hybrid model was used to detect and diagnose faults with electronic expansion valve sticking,leaking,and a four-way valve.The results showed that the hybrid model proposed in this paper could effectively and reliably diagnose faults.The integrated correct rate of fault diagnosis reached a peak value of99.27%.In particular,the correct rate of electronic expansion valve sticking fault diagnosis was improved by8%.In addition,the hybrid model obviously improved the detection and diagnosis efficiency,decreasing the operating time by52.17%.