A knowledge-guided and data-driven method for building HVAC systems fault diagnosis

Li, Tingting; Zhao, Yang; Zhang, Chaobo; Luo, Jing; Zhang, Xuejun

doi:10.1016/j.buildenv.2021.107850

Cited by 64 publications

(13 citation statements)

References 37 publications

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“…The diagnostic process in HVAC systems, through CBM, is mainly implemented using data-driven models; a data-driven model for deploying fault detection and diagnostics processes for air handling units is presented in [11,12]. These processes are also developed using a combination of knowledge-guided and data-driven methods, which obtains a promising performance in HVAC systems [13]. The fault detection and diagnostics processes, based on the use of generative adversarial networks (GANs), is presented for HVAC systems in [14].…”

Section: Discussionmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.

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Section: Discussionmentioning

confidence: 99%

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

et al. 2021

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“…If the structure needs to be clarified, expert knowledge can be used to learn the structure of the model and predict the variables. BNs can be built manually based on expert prior knowledge or used the entire dataset to develop a machine learning model [21,[186][187][188].…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…Li et al [21] presented a fault diagnosis framework for HVAC systems using a combination of data-driven methods and expert knowledge. They collected data from three sensors (temperature, pressure, and flow rate) on an AHU in a commercial building.…”

Section: Hybrid Methodsmentioning

confidence: 99%

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Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Hodavand,

Ramaji,

Sadeghi

2023

Buildings

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Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

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“…Diagnostics detects, isolates, and localizes a faulty component based on a failure model (FM) [7] [8]. Diagnostics for heating, ventilation, and air conditioning (HVAC) systems, the system of interest in this paper, is developed in [8] [9] [10] [11] [12]. Prognostics is performed by assessing changes in the behaviour of components or systems over time to predict their remaining useful life (RUL) and end of life (EoL).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

2022

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Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components' degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate runto-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components' health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter's run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.INDEX TERMS Prognostics and health management, Hybrid Modelling, Deep learning, HVAC system, railway.

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A knowledge-guided and data-driven method for building HVAC systems fault diagnosis

Cited by 64 publications

References 37 publications

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

A Hybrid Model-Based Approach on Prognostics for Railway HVAC

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