基于PSO优化BP的冷水机组故障诊断研究
Fault Diagnosis for Centrifugal Chiller Based on PSO-BP

将PSO（粒子群算法）优化BP（误差反向传播神经网络）应用于离心式冷水机组的故障诊断，针对7种典型故障，包括4种局部故障与3种系统故障，建立PSO优化BP的诊断模型。结果表明：PSO优化后的BP神经网络（包括单隐层与双隐层）的故障诊断性能显著提高，神经网络结构简化，较少的隐含层节点即可获得较优的诊断性能。单隐层神经网络优化后最佳隐含层节点数从18降至10，诊断正确率从89.42%提升至95.30%；双隐层神经网络优化后最佳隐含层节点数从25降至12，诊断正确率从97.87%提升至98.11%，诊断用时仅为优化前的23%。故障诊断虚警率（假报及漏报）降低，且显著改善了系统故障尤其制冷剂泄漏故障的诊断性能，对正常情况的识别率亦极大提高。PSO优化有助于BP网络跳出局部极小值，较好地改善了故障诊断性能。
In this study, a BP (back-propagation network) neural network, optimized by PSO (particle swarm optimization) was applied to the fault diagnosis of a centrifugal chiller. Seven typical faults, including four component-level and three system-level faults, were investigated. Results showed that the performance of fault diagnosis was significantly improved (for both single- and double-hidden BP layers) compared with the model without PSO. The optimization simplified the structure of the neural network from 18 neurons to 10 neurons for a single-hidden-layer network and from 25 neurons to 12 neurons for a double-hidden-layer network. This increased the correct rate of fault diagnosis from 89.42% to 95.30% and from 97.87% to 98.11% for single-hidden-layer network and double-hidden-layer network, respectively. There are also considerable savings in diagnostic time, especially for the double-hidden–layer network, to only 23% of that before optimization. The cases of "false report" and "leaked report" have been reduced, and the false alarm rate is also lower than before. Moreover, the diagnosis performance of the system-level fault, especially the RefLeak (Refrigerant Leakage), and the recognition rate of the normal condition are greatly improved. Through PSO, the BP network is able to jump out of the local minimum and greatly improve the fault diagnosis performance for the centrifugal chiller