Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Albayati, Mohammed G.; Faraj, Jalal; Thompson, Amy; Patil, Prathamesh; Gorthala, Ravi; Rajasekaran, Sanguthevar

doi:10.26599/bdma.2022.9020015

Cited by 19 publications

(5 citation statements)

References 27 publications

(59 reference statements)

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“…Similarly, Fan et al [166] addressed the limitation of collecting faulty label data by investigating semi-supervised methods. They proposed a semi-supervised FDD method based on neural networks for AHUs collected from the ASHRAE research project RP-1312.…”

Section: Semi-supervised Methodsmentioning

confidence: 99%

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Hodavand,

Ramaji,

Sadeghi

2023

Buildings

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Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

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Section: Semi-supervised Methodsmentioning

confidence: 99%

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Hodavand,

Ramaji,

Sadeghi

2023

Buildings

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“…Similarly to Shahnazari et al, Albayati et al [132] used an ML-based approach to identify faults in a roof top unit. SVMs were principally applied to find seven categories of faults and their impacts in a heating, cooling and ventilating system under standard use, considering an industrial building in Connecticut.…”

Section: Fault Detectionmentioning

confidence: 99%

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

Villano,

Mauro,

Pedace

2024

Thermo

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Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a precious tool to simulate and optimize building energy performance, as shown by a plethora of recent studies. Accordingly, this paper provides a review of more than 70 articles from recent years, i.e., mostly from 2018 to 2023, about the applications of machine/deep learning (ML/DL) in forecasting the energy performance of buildings and their simulation/control/optimization. This review was conducted using the SCOPUS database with the keywords “buildings”, “energy”, “machine learning” and “deep learning” and by selecting recent papers addressing the following applications: energy design/retrofit optimization, prediction, control/management of heating/cooling systems and of renewable source systems, and/or fault detection. Notably, this paper discusses the main differences between ML and DL techniques, showing examples of their use in building energy simulation/control/optimization. The main aim is to group the most frequent ML/DL techniques used in the field of building energy performance, highlighting the potentiality and limitations of each one, both fundamental aspects for future studies. The ML approaches considered are decision trees/random forest, naive Bayes, support vector machines, the Kriging method and artificial neural networks. The DL techniques investigated are convolutional and recursive neural networks, long short-term memory and gated recurrent units. Firstly, various ML/DL techniques are explained and divided based on their methodology. Secondly, grouping by the aforementioned applications occurs. It emerges that ML is mostly used in energy efficiency issues while DL in the management of renewable source systems.

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“…This approach effectively optimizes learning from limited data while further strengthening the fault diagnosis by integrating various classifiers, which can help mitigate the risk of incorporating noise or irrelevant features. This can increase the diversity and robustness of the learning process [20]. Despite these advances, a critical challenge remains in the training model process.…”

Section: Background and Related Workmentioning

confidence: 99%

A Fault Detection System for Wiring Harness Manufacturing Using Artificial Intelligence

Song,

Kumar,

Kim

et al. 2024

Mathematics

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Due to its simplicity, accuracy, and adaptability, Crimp Force Monitoring (CFM) has long been the standard for fault detection in wiring harness manufacturing. However, it necessitates frequent reconfigurations based on the variability in materials, dependency on operator skill, and high costs of implementation, and thus reconfiguration presents significant challenges. To solve these problems, this paper introduces a fault detection system that employs an Artificial Intelligence (AI) classification model to enhance the performance and cost-efficiency of the quality control process of wiring harness manufacturing. Since there are no labeled data to train the classification model at the onset of manufacturing, a small number of normal data from each production run are manually extracted to train the model. To address the constraint of the limited available data, the system generates synthetic data from normal data, simulating potential defects by using Regional Selective Data Scaling (RSDS). This innovative method performs upscaling or downscaling on specific regions of the original data to produce synthetic abnormal data, which enables the fault detection system to efficiently train its classification model with a dataset consisting solely of normal operation data.

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Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Cited by 19 publications

References 27 publications

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

A Review on Machine/Deep Learning Techniques Applied to Building Energy Simulation, Optimization and Management

A Fault Detection System for Wiring Harness Manufacturing Using Artificial Intelligence

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