基于贝叶斯网络的变风量末端故障诊断方法*
Fault diagnosis method of variable air volume terminals based on Bayesian network
摘要：
针对压力无关再热型变风量末端的15种典型故障，提出了一种基于贝叶斯网络的故障诊断方法。根据某建筑实际运行系统建立了Dymola仿真模型，并基于模拟故障数据对所提出的诊断方法进行了验证。结果表明，该方法对于绝大多数故障都可以成功检测并分离，有着较高的准确性和可靠性，并且可较好地应对实际工程中存在的数据问题，将实时故障诊断的应用场景进一步推广。
Abstract：
Aiming at 15 typical faults of the pressure independent variable air volume (VAV) terminal with reheat coil, proposes a Bayesian network-based fault diagnosis method (FDD). Establishes a Dymola simulation model based on an actual VAV system, and verifies the performance of the proposed method based on simulated fault data. The results show that this method performs well at: (1) detecting and isolating most faults with high accuracy and reliability, (2) dealing with the data problems existing in actual engineering, (3) further popularizing the application of real-time fault diagnosis.
Keywords:faultdiagnosis,Bayesiannetwork,pressureindependentVAVterminalwithreheatcoil,diagnosticaccuracy,detectionaccuracy