HVAC System Fault Detection Diagnosis and Identification

虽然能源管理和控制系统已经做出了贡献， 显著减少（20-40%）建筑物中的能源使用 在不牺牲乘员舒适性的前提下， 还没有完全实现。 这主要是因为他们无法 快速检测和补偿加热系统中的故障 通风和空调（HVAC）系统。 事实上，无论如何， 好的控制方案，为暖通空调系统可能是，存在 未检测到的故障可能会完全消除任何预期的节省。 这 研究的重点是故障检测与诊断问题， HVAC系统。 该项目的主要目标是开发一个 一种基于扩展卡尔曼滤波和最大似然估计的有效方法 用于检测和诊断的似然系统识别 空调系统故障，并证明其有效性，通过 应用于空气处理器系统。 方法论是 强大到足以允许快速在线故障检测和诊断 并且还可以在个人计算机上实现。 此外，彩色图形 显示能力将被纳入，以帮助操作员 监控系统的状态。 加强第三阶段 商业化的努力，将遵循，一个原型系统将 开发和测试。

Although Energy Management and Control Systems have contributed significantly to the reduction (20-40%) of energy use in buildings without sacrificing occupants' comfort, their full capabilities have not been completely realized. This is primarily due to their inability to quickly detect and compensate for failures in the Heating Ventilating and Air Conditioning (HVAC) system. In fact, no matter how good the control scheme for the HVAC system might be, the presence of undetected faults can completely wipe out any expected savings. This research focuses on the problem of fault detection and diagnosis of HVAC systems. The primary goal of this project is to develop an efficient methodology based on extended Kalman filtering and Maximum Likelihood system identification for the detection and diagnosis of faults in HVAC systems and to demonstrate its effectiveness through application to an air handler system. The methodology is to be powerful enough to permit rapid on-line fault detection and diagnosis and yet be implementable on a personal computer. Also, color graphic display capability is to be incorporated to aid the operator in monitoring the state of the system. To enhance the Phase III commercialization effort that will follow, a prototype system will be developed and tested.