A robust online fault detection and diagnosis strategy of centrifugal chiller systems for building energy efficiency

2015 ◽  
Vol 108 ◽  
pp. 441-453 ◽  
Author(s):  
Dinh Anh Tuan Tran ◽  
Youming Chen ◽  
Minh Quang Chau ◽  
Baisong Ning
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Building Energy ◽  
Fault Detection And Diagnosis ◽  
Building Energy Efficiency ◽  
Detection And Diagnosis ◽  
Centrifugal Chiller

scholarly journals Fault detection and diagnosis in building energy systems: A tool chain for the automated generation of training data

2021 ◽  
Vol 2042 (1) ◽  
pp. 012083
Author(s):  
Christine van Stiphoudt ◽  
Florian Stinner ◽  
Gerrit Bode ◽  
Alexander Kümpel ◽  
Dirk Müller
Keyword(s):  
Fault Detection ◽  
Energy Systems ◽  
Building Energy ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Real Component ◽  
Energy Management Systems ◽  
Building Energy Systems ◽  
Code Extension ◽  
Detection And Diagnosis

Abstract The application of fault detection and diagnosis (FDD) algorithms in building energy management systems (BEMS) has great potential to increase the efficiency of building energy systems (BES). The usage of supervised learning algorithms requires time series depicting both nominal and component faulty behaviour for their training. In this paper, we introduce a method that automates Modelica code extension of BES models in Python with fault models to approximate real component faults. The application shows two orders of magnitude faster implementation compared to manual modelling, while no errors occur in the connections between fault and component models.

scholarly journals Fault Detection and Efficiency Assessment for HVAC Systems Using Non-Intrusive Load Monitoring: A Review

Energies ◽  
2022 ◽  
Vol 15 (1) ◽  
pp. 341
Author(s):  
Amir Rafati ◽  
Hamid Reza Shaker ◽  
Saman Ghahghahzadeh
Keyword(s):  
Energy Efficiency ◽  
Fault Detection ◽  
Research Area ◽  
Hvac Systems ◽  
Fault Detection And Diagnosis ◽  
Future Research ◽  
Efficiency Assessment ◽  
Load Monitoring ◽  
Detection And Diagnosis ◽  
Inefficient Operation

Heat, ventilation, and air conditioning (HVAC) systems are some of the most energy-intensive equipment in buildings and their faulty or inefficient operation can significantly increase energy waste. Non-Intrusive Load Monitoring (NILM), which is a software-based tool, has been a popular research area over the last few decades. NILM can play an important role in providing future energy efficiency feedback and developing fault detection and diagnosis (FDD) tools in smart buildings. Therefore, the review of NILM-based methods for FDD and the energy efficiency (EE) assessment of HVACs can be beneficial for users as well as buildings and facilities operators. To the best of the authors’ knowledge, this paper is the first review paper on the application of NILM techniques in these areas and highlights their effectiveness and limitations. This review shows that even though NILM could be successfully implemented for FDD and the EE evaluation of HVACs, and enhance the performance of these techniques, there are many research opportunities to improve or develop NILM-based FDD methods to deal with real-world challenges. These challenges and future research works are also discussed in-depth.

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