scholarly journals Development and Validation of a Data-Driven Fault Detection and Diagnosis System for Chillers Using Machine Learning Algorithms

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1945
Author(s):  
Icksung Kim ◽  
Woohyun Kim
Keyword(s):  
Fault Detection ◽  
Classification Accuracy ◽  
Energy Savings ◽  
Cost Savings ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Data Driven ◽  
Classification Methods ◽  
Detection And Diagnosis

Fault detection and diagnosis (FDD) systems enable high cost savings and energy savings that could have economic and environmental impact. This study aims to develop and validate a data-driven FDD system for a chiller. The system uses historical operation data to capture quantitative correlations among system variables. This study evaluated the effectiveness and robustness of eight FDD classification methods based on the experimental data of the chiller (the ASHRAE 1043-RP project). The training data used for the FDD system is classified into four cases. Moreover, true and false positive rates are used to characterize the performance of the classification methods. The results show that local fault is not significantly sensitive to training data, and shows high classification accuracy for all cases. The system fault has a significant effect on the amount of data and the severity levels on the classification accuracy.

scholarly journals Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yimin Chen ◽  
Jin Wen
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Field Evaluation ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Component Level ◽  
Automation Systems ◽  
Tightly Coupled ◽  
Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

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 Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

2022 ◽  
Vol 2022 ◽  
pp. 1-48
Author(s):  
Michael Yit Lin Chew ◽  
Ke Yan
Keyword(s):  
Fault Detection ◽  
Real World ◽  
Data Science ◽  
Building Design ◽  
Facility Management ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Main Concern ◽  
Smart Building ◽  
Detection And Diagnosis

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

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