Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis

Du, Zhimin; Fan, Bo; Jin, Xinqiao; Chi, Jinlei

doi:10.1016/j.buildenv.2013.11.021

Cited by 261 publications

(84 citation statements)

References 24 publications

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“…After being cooled down by the chilled water, the supply air is delivered to each air conditioning zone by the variable-speed supply fan. Moreover, the return air is divided into two streams by the variable-speed return fan: one stream is exhaust air to the outside of the building, and the other is recycled in the next air circulation (Du et al, 2014). The supply fan speed is regulated based on the duct static pressure.…”

Section: Nomenclaturementioning

confidence: 99%

Fault Detection and Diagnosis with Modelica Language using Deep Belief Network

Lee

Shin

2015

Linköping Electronic Conference Proceedings

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The air handling unit (AHU) is the main component of heating, ventilation and airconditioning (HVAC) systems, and irregular faults in AHUs are major sources of energy consumption. For energy efficient operation of HVAC, this paper aims to detect and diagnose three abnormal states in the AHU with the popular deep learning model, called Deep Belief Network (DBN), where we train it using various data generated by Modelica.

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Section: Nomenclaturementioning

confidence: 99%

Fault Detection and Diagnosis with Modelica Language using Deep Belief Network

Lee

Shin

2015

Linköping Electronic Conference Proceedings

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“…Its basic aim is to detect outlier that may represent fault in an HVAC system. According to Du et al (2014b), in general, FDD methods can be divided into three categories: the rules-based, model-based and data-driven methods. Model-based can be developed by employing energy and mass balance phenomenon and residues can be calculated by comparing outputs from the model and actual measurements.…”

Section: Hvac Systems Optimization and Fault Detection And Diagnosismentioning

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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“…In the past several decades, it has been successfully applied in economics, retails, telecommunication, and financial services [4]. Recently, efforts have also been made to investigate the application of data mining in HVAC field, including building energy consumption prediction [5,6], building energy management [7,8], fault detection and diagnosis [9,10], and occupant behaviour [11,12].…”

Section: Technical Approachmentioning

confidence: 99%

An application of data mining in district heating substations for improving energy performance

et al. 2017

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Abstract. Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

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Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis

Cited by 261 publications

References 24 publications

Fault Detection and Diagnosis with Modelica Language using Deep Belief Network

Fault Detection and Diagnosis with Modelica Language using Deep Belief Network

Computational intelligence techniques for HVAC systems: A review

An application of data mining in district heating substations for improving energy performance

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