暖通空调系统通常在建筑中消耗最大的能源，尤其是在商业领域。据报道，商业建筑占美国全国能源消耗的近20%，或每年全球温室气体排放的12%。商业建筑中15%到30%的能源浪费是由于性能下降、控制策略不当以及暖通空调系统和设备故障造成的。本文将基于统计机器学习的故障诊断方法与数据融合方法相结合，提出了一种新的故障检测与诊断方法。该方法还包括聚类方法和优化技术，以避免建模过程收敛到局部极小值。 通过训练多个隐马尔可夫模型（HMM）来模拟不同的故障类别，并采用聚类算法来提高FDD的精度。该方法已在一栋带有多个AHU的商业建筑上成功试用。它不仅可以识别在训练过程中建模的系统故障，还可以用于诊断。初步的实验结果证明了该方法的有效性。引用:德克萨斯州达拉斯ASHRAE会议论文。

HVAC systems typically consume the largest protion of energy in buildings, particluatry in the commercial sector. It is reported that commercial buildings account for almost 20% of the US national energy consumption or 12% of the national contribution to annual global greenhouse gas emissions. From 15% to 30% of the energy waste in commercial buildings is due to performance degradation, improper control strategies and malfunction of HVAC systems and equipment. This paper proposed a new fault detection and diagnosis (FDD) approach by applying a statistical machine learning based FDD method with data fusion methods. The approach also includes clustering methods and an optimization technique to avoid the modeling process converging to local minimum. A number of hidden markov models (HMMs) are trained to model different catalogues of faults, and a clustering algorithm is applied to enhance the FDD accuracy. This approach has been successfully trialed on one commercial building with multiple AHUs. It can not only identify system faults that were modeled within the training process, but also can be applied for diagnosis. Preliminary experimental results are demonstrating effective performance.