Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

Zhao, Yang; Wen, Jin; Wang, Shengwei

doi:10.1016/j.applthermaleng.2015.07.001

Cited by 114 publications

(31 citation statements)

References 22 publications

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“…The development of Bayesian network for this study required to estimate parameters (which represented the quantitative probabilistic relationship between layers) and it was acknowledged in the study that obtaining these parameters are difficult, and further work needs to be done to reduce this difficulty. Same kind of methodology was also employed by Zhao et al (2015) and Xiao et al (2014) but for diagnosing faults in air handling units and variable air volume systems respectively. Naja.…”

Section: Bayesian Networkmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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Section: Bayesian Networkmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…Morgan et al [14] used the feature values obtained from oil analysis to establish a BN to identify diesel engine faults. Zhao et al [15] defined fault diagnosis disciplines using the BNs to identify the faults of air handling devices. Lu et al [16] input the feature values extracted from the vibration signal into the BP neural network for identifying the turbine fault.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and diagnosis method of diesel engine by combining rule-based algorithm and Bayesian/neural networks

Cai

Sun

Wang

et al. 2020

Preprint

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The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring and identification of faults are of great significance. At present, the fault research on diesel engine still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a constant speed. Therefore, a rule-based algorithm for fault diagnosis is proposed. Bayesian networks (BNs) and BP neural networks are used to identify seven faults at different speeds. Changchai EV80 diesel engine is taken as an example, and the feature values are extracted from the vibration signals measured from the cylinder head. The signals are processed by wavelet threshold de-noising and Ensemble Empirical Mode Decomposition (EEMD). The signal-sensitive feature values extracted from the decomposed Intrinsic Mode Function are used to distinguish different faults. After obtaining the feature values, a rule-based algorithm using IF... THEN's logic statement is proposed. BNs and BP neural networks established by parameter learning method are used for fault identification. Furthermore, this paper considers the uncertain factors and the interference of the external environment. Gaussian white noise is added to the raw signal and external excitation interference is applied to the diesel engine when it is running under normal operation condition. The results show that the proposed fault diagnostic method can accurately identify the faults.

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“…Common classification methods used in AFDD systems include decision tree, Bayesian Network classifier, geometrical classifier and artificial neural network classifier. One of the most commonly used classifiers in building AFDD is the artificial neural network classifier (ANN) [86], [93]- [96] and Bayesian Network (BN) classifier [76], [82], [97]- [99]. Geometrical classifiers have also been used in some fault diagnosis applications [96], [100], [101].…”

Section: Classification Methodsmentioning

confidence: 99%

“…Unfortunately, only the cooling season data and spring season data is used in this case study due to some measurement issues reported during the heating season. As mentioned in the methodology chapter, Zhao et al [99] have previously used the same dataset to demonstrate the FDD results with an event-based Bayesian Network, which will be used as a 3. Return fan performance: a fully closed return fan and a lower performance return fan could cause the AHU not able to condition the whole building due to decreased air circulation.…”

Section: Ashrae Rp-1312mentioning

confidence: 99%

“…Cyclic and acyclic graphical probabilistic modelsMost of the BNs employed in engineering system FDD are two layer networks (symptom-faults)[99], while in medical diagnosis applications the network is more complex and external facts such as age and gender are part of the diagnosis. Also, most of the faults inside engineering systems are affected by externalities such as time since last maintenance; those factors should be included in the DBN as well.In this thesis, 1 , … , denote faults, while lower case 1 , … , are the fault states.…”

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confidence: 99%

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A Probabilistic Distributed Fault Detection, Diagnostics and Evaluation Framework for Building Systems

Shi¹

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The scope of this thesis is to develop an automated fault detection, diagnostic, and evaluation (AFDDE) framework for building systems. This framework aims to provide a holistic approach to detect, identify and evaluate building faults to the stakeholders to facilitate decision-making. It is adaptable to different building systems as well as flexible to both distributed and centralised implementations. The first component of the framework, fault detection, uses a novel technique called constrained dual Extended Kalman Filter (EKF) to estimate system parameters and then generates symptom descriptions described by probability and severity. The fault diagnostic process uses Dynamic Bayesian Network (DBN) with leaky Noisy-Max model to accommodate probabilistic descriptions of faults and symptoms. The fault evaluation aspect of the system employs existing building performance simulation (BPS) tools to estimate quantitative impacts of the diagnosed faults. A model reduction process called "model-cluster-reduce" is also developed to speed up simulation. Each component of the framework is created with the intention to be generalized to other related areas of research such as model predictive control and BPS optimization. Four case studies of both zone-level and air handling unit (AHU)-level are adopted to demonstrate the functionalities of the proposed AFDDE framework. Overall, the framework shows promising results with a short fault diagnosis time, and low false positive and false negative rates, albeit with the tendency of overestimating fault impacts.In addition to the future work to further expand the AFDDE framework, many fundamental research questions also arise from this thesis.iii Acknowledgements First and foremost, I would like to thank my Ph.D. supervisor, Dr. Liam O'Brien for his guidance and supervision throughout my study. It was a great pleasure to work with him, and I would not have accomplished this work without his mentoring and encouragement.He has aspired me to continue my academic career and pursue my research in building science.

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Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

Cited by 114 publications

References 22 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Fault detection and diagnosis method of diesel engine by combining rule-based algorithm and Bayesian/neural networks

A Probabilistic Distributed Fault Detection, Diagnostics and Evaluation Framework for Building Systems

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