Tun Wunna, Wong Kwok-Wai Johnny, Ling Sai-Ho
Faculty of Design, Architecture and Building, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Sensors (Basel). 2023 Sep 6;23(18):7690. doi: 10.3390/s23187690.
Efficiency and comfort in buildings rely on on well-functioning HVAC systems. However, system faults can compromise performance. Modern data-driven fault detection methods, considering diverse techniques, encounter challenges in understanding intricate interactions and adapting to dynamic conditions present in HVAC systems during occupancy periods. Implementing fault detection during active operation, which aligns with real-world scenarios and captures dynamic interactions and environmental changes, is considered highly valuable. To address this, utilizing the dynamic simulation system HVAC SIMulation PLUS (HVACSIM+), an HVAC fault model was developed using 194 sensor signals from each HVAC component within a single-story, four-room building. The advanced HVAC fault detection framework, leveraging simulated HVAC operational scenarios with the Gramian angular field (GAF) and two-dimensional convolutional neural networks (GAF-2DCNNs), offers a robust and proactive solution. By utilizing the GAF capacity to convert time-series sensor data into informative 2D images, integrated with 2DCNN for automated feature extraction, hidden temporal relationships within 1D signals are captured. After training on nine significant HVAC faults and normal conditions during occupancy, the effectiveness of the proposed GAF-2DCNN is evaluated through comparisons with support vector machine (SVM), random forest (RF), and hybrid RF-SVM, one-dimensional convolutional neural networks (1D-CNNs). The results demonstrates an impressive overall accuracy of 97%, accompanied by precision, recall, and F1 scores that surpass 90% for individual HVAC faults. Through the introduction of the unified approach that integrates HVACSIM+ simulated data and GAF-2DCNN, a notable enhancement in robustness and reliability for handling substantial HVAC faults is achieved.
建筑物的效率和舒适度依赖于运行良好的暖通空调（HVAC）系统。然而，系统故障会影响其性能。现代数据驱动的故障检测方法，尽管技术多样，但在理解复杂的相互作用以及适应HVAC系统在使用期间的动态条件方面面临挑战。在实际运行中实施故障检测，这与现实场景相符，并能捕捉动态相互作用和环境变化，被认为具有很高的价值。为了解决这个问题，利用动态仿真系统HVAC SIMulation PLUS（HVACSIM+），基于一栋单层四室建筑内每个HVAC组件的194个传感器信号，开发了一个HVAC故障模型。先进的HVAC故障检测框架，利用格拉姆角场（GAF）和二维卷积神经网络（GAF - 2DCNN）模拟HVAC运行场景，提供了一个强大且主动的解决方案。通过利用GAF将时间序列传感器数据转换为信息丰富的二维图像的能力，并与二维卷积神经网络集成以进行自动特征提取，捕捉一维信号中隐藏的时间关系。在针对使用期间的九个重大HVAC故障和正常情况进行训练后，通过与支持向量机（SVM）、随机森林（RF）以及混合RF - SVM、一维卷积神经网络（1D - CNN）进行比较，评估了所提出的GAF - 2DCNN的有效性。结果显示总体准确率高达97%，令人印象深刻，并且对于单个HVAC故障，精确率、召回率和F1分数均超过90%。通过引入整合HVACSIM+模拟数据和GAF - 2DCNN的统一方法，在处理大量HVAC故障时的鲁棒性和可靠性得到了显著提高。
