Current methods for energy diagnosis in heating, ventilation and air conditioning (HVAC) systems are not consistent with process and instrumentation diagrams (P&IDs) as used by engineers to design and operate these systems, leading to very limited application of energy performance diagnosis in practice. In a previous paper, a generic reference architecture – hereafter referred to as the 4S3F (four symptoms and three faults) framework – was developed. Because it is closely related to the way HVAC experts diagnose problems in HVAC installations, 4S3F largely overcomes the problem of limited application. The present article addresses the fault diagnosis process using automated fault identification (AFI) based on symptoms detected with a diagnostic Bayesian network (DBN). It demonstrates that possible faults can be extracted from P&IDs at different levels and that P&IDs form the basis for setting up effective DBNs. The process was applied to real sensor data for a whole year. In a case study for a thermal energy plant, control faults were successfully isolated using balance, energy performance and operational state symptoms. Correction of the isolated faults led to annual primary energy savings of 25%. An analysis showed that the values of set probabilities in the DBN model are not outcome-sensitive.

当前在供暖，通风和空调（HVAC）系统中进行能量诊断的方法与工程师用来设计和操作这些系统的过程图和仪表图（P＆ID）不一致，因此在实践中对能量性能诊断的应用非常有限。在上一篇论文中，开发了通用参考体系结构（以下称为4S3F（四个症状和三个故障）框架）。由于4S3F与HVAC专家诊断HVAC安装中的问题的方式密切相关，因此在很大程度上克服了应用受限的问题。本文基于诊断贝叶斯网络（DBN）检测到的症状，使用自动故障识别（AFI）解决了故障诊断过程。它表明可以从P＆P中提取可能的故障 不同级别的ID以及P＆ID是设置有效DBN的基础。该过程将应用于整个一年的真实传感器数据。在一个热电厂的案例研究中，使用平衡，能量性能和运行状态症状成功隔离了控制故障。隔离故障的纠正导致每年一次能源节省25％。分析表明，DBN模型中设置概率的值对结果不敏感。