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【Applied Energy最新原创论文】面向大型复杂空调系统高维传感器故障检测的热力学定律嵌入式深度学习方法

AEii国际应用能源  · 公众号  ·  · 2023-09-23 21:03

正文

原文信息:

A thermodynamic-law-integrated deep learning method for high-dimensional sensor fault detection in diverse complex HVAC systems

原文链接:

https://www.sciencedirect.com/science/article/pii/S0306261923011947

Highlights

(1)本文提出了一种全新可靠的高维传感器故障检测方法。

(2)该方法利用损失函数在深度学习中嵌入热力学定律。

(3)嵌入式热力学定律能有效消除故障数据在神经网络中的负面传播影响。

(4)该方法可有效提升复杂多故障情况下的检测准确性与可靠性。

Research gap

大型复杂中央空调系统中所配备的传感器日益增多,其健康运行是系统能效及室内热舒适的基本保障。传统故障检测方法难以在大量传感器构成的高维数据中高效、精确识别故障;同时新兴深度学习方法虽善于处理高维数据,但因其纯数据驱动本质,可靠性难以保障。因此,本研究提出了一种热力学定律嵌入式深度学习方法,有效提升检测可靠性,解决复杂空调系统中高维传感器故障检测难题。

在大型中央空调系统中往往安装了大量传感器,用于保障空调系统的高效运行及室内热舒适。如何在大量传感器所产生的高维数据中,特别是复杂多故障并存的情况下,高效、精确、可靠地检测传感器故障,是一个具有挑战性的问题。在本研究中,我们创新性地提出了一种热力学定律(包括质量守恒与能量守恒)嵌入式深度学习方法,用以解决此问题。该方法利用深度学习的智能性、灵活性与高效性,有效处理高维数据所带来复杂性。

更重要的是,我们的嵌入方案使得深度学习模型以显性方式学习系统运行中的热力学规律,有效减少或消除单纯深度学习方法中常观测到的不合理结果(如违反质量守恒或能量守恒等结果),进而提升高维传感器故障检测的准确性和可靠性。

在所讨论案例中,与传统的单一深度学习方法相比,嵌入热力学定律的深度学习方法在复杂的多故障场景下,故障检测率提高了27.2%,误报率显著降低了77.4%。进一步分析表明,当多个传感器发生故障时,热力学定律的嵌入可以大大减轻在深度神经网络内部由故障数据传播所造成的负面影响。本文提供了一种有效且可靠的高维传感器故障检测方案,可确保大型复杂空调系统中数量与日俱增的传感器健康运行。


更多关于“Fault detection ”的研究请见:https://www.sciencedirect.com/search?pub=Applied%20Energy&cid=271429&qs=Fault%20detection

Abstr act

In building Heating, Ventilation and Air Conditioning (HVAC) systems, sensor healthy operation is the foundation of the adopted control strategies to improve building energy efficiency and indoor thermal comfort. For large and complex HVAC systems where a large number of sensors are often installed, associated sensor fault detection is highly challenging due to the high dimensionality of the sensor data and complex multiple-fault scenarios. To address this challenging issue, this study proposes a novel method in which the thermodynamic laws (i.e., mass balance and energy conservation) are integrated with deep learning. By making use of the intelligence, flexibility, and efficiency of deep learning, the proposed method can easily handle high-dimensional sensor measurements. More importantly, the integration enables the thermodynamic laws (which govern the mass and heat transfer processes in HVAC systems) to be explicitly learned and thus can effectively reduce/eliminate unreasonable results (e.g., violations of mass balance or energy conservation) frequently observed from sole deep learning methods due to their pure data-driven nature. Reduction/elimination of such unreasonable results can improve associated high-dimensional sensor fault detection performance in terms of accuracy and reliability. In the case study, compared with a conventional sole deep learning method, the proposed method increased the fault detection rate by 27.2%, and significantly reduced the false alarm rate by 77.4% in the complex multi-fault scenarios. Associated analysis demonstrated that the integration of thermodynamic laws can substantially alleviate the adverse intercorrelation impacts induced by faulty measurements inside the deep neural network when multiple sensor faults occurred. The proposed method provides an effective and reliable means to ensure the sensor healthy operation in large and complex HVAC systems in particular as increasingly more sensors are installed nowadays.


Keywords:

HVAC   暖通空调

Fault detection   故障检测

Sensor fault   传感器故障

Physics-informed deep learning   基于物理信息的深度学习

Data-driven   数据驱动

Graphics

图1 . 本文所提出热力学定律嵌入式深度学习方法总览

图2. 自编码器(Autoendocer)深度神经网络用于传感器故障检测


(a)

(b)

图3. 复杂多故障情况下,热力学定律嵌入式深度学习方法相较于单一深度学习方法实现了更高的故障检测率与更低的误报率


(a)


(b)







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