With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intri...With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.展开更多
Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time.Today,most building owners are per...Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time.Today,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs.This is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on findings.Commercially available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system reliability.However,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results.In this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance.The study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in Connecticut.This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.展开更多
Fault detection in district heating(DH)substations is crucial for maintaining energy efficiency.However,existing methods often fall short and rely on labelled data or global analysis that may miss subtle anomalies.We ...Fault detection in district heating(DH)substations is crucial for maintaining energy efficiency.However,existing methods often fall short and rely on labelled data or global analysis that may miss subtle anomalies.We introduce HEAT,a Hierarchical-constrained Encoder-Assisted Time series clustering method designed to enhance fault detection in DH substations.HEAT operates in a two-phase approach:first,it approximates a relative network topology using a constraint hierarchical clustering algorithm on supply temperature profiles.HEAT incorporates a Convolutional AutoEncoder(CAE)for dimensionality reduction of the time series data and uses adaptive soft constraints in the linkage function,enabling both minimum and maximum cluster size constraints while supporting domain knowledge,e.g.,must-link and cannot-link constraints,using a constraint matrix.Second,we use the topology approximation to perform intra-cluster analysis using Mean Absolute Deviation(MAD)z-scores,with neighbouring substations serving as a validation mechanism,allowing for robust analysis without requiring labelled data.Experimental results demonstrate that HEAT outperforms conventional clustering methods while achieving 74.1%sensitivity and 95.5%specificity in fault detection,significantly improving over typical global analysis.HEAT not only identified major faults(e.g.,sensor issues,valve failures)but also detected subtle anomalies(e.g.,secondary leakages)while minimising false positives.This unsupervised method offers a viable and flexible solution for DH networks,improving operational efficiency and energy sustainability without disclosing sensitive information.展开更多
The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supe...The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.展开更多
基金supported by the National Natural Science Foundation China under Grants number 62073232,and the Science and Technology Project of Shenzhen,China(KCXST20221021111402006,JSGG20220831105800002)and the“Nanling Team Project”of Shaoguan city,and the Science and Technology project of Tianjin,China(22YFYSHZ00330)+1 种基金and Shenzhen Excellent Innovative Talents RCYX20221008093036022,Shenzhen-HongKong joint funding project(A)(SGDX20230116092053005)the Shenzhen Undertaking the National Major Science and Technology Program,China(CJGJZD20220517141405012).
文摘With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.
基金supported in part by the US Department of Energy(No.DE-EE0008189)and the National Science Foundation(Nos.1743418 and 1843025).
文摘Most heating,ventilation,and air-conditioning(HVAC)systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time.Today,most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs.This is mainly because the building owners do not previously have good tools to detect and diagnose these faults,determine their impact,and act on findings.Commercially available fault detection and diagnostics(FDD)tools have been developed to address this issue and have the potential to reduce equipment downtime,energy costs,maintenance costs,and improve occupant comfort and system reliability.However,many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results.In this paper,supervised and semi-supervised machine learning(ML)approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance.The study data was collected from one packaged rooftop unit(RTU)HVAC system running under normal operating conditions at an industrial facility in Connecticut.This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning,achieving accuracies as high as 95.7%using few-shot learning.
文摘Fault detection in district heating(DH)substations is crucial for maintaining energy efficiency.However,existing methods often fall short and rely on labelled data or global analysis that may miss subtle anomalies.We introduce HEAT,a Hierarchical-constrained Encoder-Assisted Time series clustering method designed to enhance fault detection in DH substations.HEAT operates in a two-phase approach:first,it approximates a relative network topology using a constraint hierarchical clustering algorithm on supply temperature profiles.HEAT incorporates a Convolutional AutoEncoder(CAE)for dimensionality reduction of the time series data and uses adaptive soft constraints in the linkage function,enabling both minimum and maximum cluster size constraints while supporting domain knowledge,e.g.,must-link and cannot-link constraints,using a constraint matrix.Second,we use the topology approximation to perform intra-cluster analysis using Mean Absolute Deviation(MAD)z-scores,with neighbouring substations serving as a validation mechanism,allowing for robust analysis without requiring labelled data.Experimental results demonstrate that HEAT outperforms conventional clustering methods while achieving 74.1%sensitivity and 95.5%specificity in fault detection,significantly improving over typical global analysis.HEAT not only identified major faults(e.g.,sensor issues,valve failures)but also detected subtle anomalies(e.g.,secondary leakages)while minimising false positives.This unsupervised method offers a viable and flexible solution for DH networks,improving operational efficiency and energy sustainability without disclosing sensitive information.
基金support of this research by the National Natural Science Foundation of China (No.52278117)the Philosophical and Social Science Program of Guangdong Province,China (GD22XGL20)the Shenzhen Science and Technology Program (No.20220531101800001 and No.20220810160221001).
文摘The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and diagnosis.In practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled data.To tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum decision-makings.More specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault classifications.Data experiments have been designed to evaluate the values of the methods proposed.Three datasets on HVAC air-side operations have been used to ensure the generalizability of results obtained.Different data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their architectures.The research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.
