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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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  • Haoshan Ren
  • Chengliang Xu
  • Yuanli Lyu
  • Zhenjun Ma
  • Yongjun Sun


Original languageEnglish
Article number121830
Journal / PublicationApplied Energy
Online published29 Aug 2023
Publication statusPublished - 1 Dec 2023


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. © 2023 Elsevier Ltd

Research Area(s)

  • Data-driven, Fault detection, HVAC, Physics-informed deep learning, Sensor fault