Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

Gharsellaoui, Sondes; Mansouri, Majdi; Trabelsi, Mohamed; Harkat, Mohamed-Faouzi; Refaat, Shady S.; Messaoud, Hassani

doi:10.1109/access.2020.3019365

Cited by 18 publications

(8 citation statements)

References 37 publications

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“…Semi-supervised learning is often used by implementing a Generative Adversarial Network (GAN), which can provide a larger amount of data and classes balance with generating a synthetic dataset [ 43 , 44 , 54 , 63 , 102 ]. The single-class (binary) classifier (SVM) is also a popular method, in which a model is trained only on healthy datasets that have been generated by GAN [ 44 , 66 ].…”

Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

“…According to the work reviewed, it is common to use physics-based models to generate synthetic data (both faulty and healthy data), followed by data-driven modeling, i.e., supervised learning of fault prediction models [ 80 , 81 , 87 , 88 , 91 , 92 , 93 , 102 ]. Simple physical models were used by Chintala et al [ 104 ] where the Kalman filter in EnergyPlus was tested using only thermostat and outdoor temperature to perform FDD of equipment deterioration.…”

Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

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A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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Heating, ventilation, and air conditioning (HVAC) systems are a popular research topic because buildings’ energy is mostly used for heating and/or cooling. These systems heavily rely on sensory measurements and typically make an integral part of the smart building concept. As such, they require the implementation of fault detection and diagnosis (FDD) methodologies, which should assist users in maintaining comfort while consuming minimal energy. Despite the fact that FDD approaches are a well-researched subject, not just for improving the operation of HVAC systems but also for a wider range of systems in industrial processes, there is a lack of application in commercial buildings due to their complexity and low transferability. The aim of this review paper is to present and systematize cutting-edge FDD methodologies, encompassing approaches and special techniques that can be applied in HVAC systems, as well as to provide best-practice heuristics for researchers and solution developers in this domain. While the literature analysis targets the FDD perspective, the main focus is put on the data-driven approach, which covers commonly used models and data pre-processing techniques in the field. Data-driven techniques and FDD solutions based on them, which are most commonly used in recent HVAC research, form the backbone of our study, while alternative FDD approaches are also presented and classified to properly contextualize and round out the review.

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Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

Section: Results Part I: Review and New Classification Of Fdd Approac...mentioning

confidence: 99%

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Matetić

Štajduhar

Wolf

et al. 2022

Sensors

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show abstract

“…The comparison between real-time data stream and learned historical patterns yields accurate operation diagnosis for a few fault types in a real building. Later in the past decade, various aspects of ML methods were intensely explored to characterize occurrences of faults, detect abnormal operating conditions, and classify fault types, such as adaptive thresholds, using t-statistic approach, fuzzy logic, ANN, Gaussian process regression, support vector machine, , gradient boosting regression, and generative adversarial network . However, these methods rarely captured temporal dependencies and dynamics of faults.…”

Section: Performance Prediction and Design Optimization Of Thermal En...mentioning

confidence: 99%

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Dai

2022

ACS Energy Lett.

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Recent advances in machine learning (ML) have impacted research communities based on statistical perspectives and uncovered invisibles from conventional standpoints. Though the field is still in the early stage, this progress has driven the thermal science and engineering communities to apply such cutting-edge toolsets for analyzing complex data, unraveling abstruse patterns, and discovering non-intuitive principles. In this work, we present a holistic overview of the applications and future opportunities of ML methods on crucial topics in thermal energy research, from bottom-up materials discovery to top-down system design across atomistic levels to multi-scales. In particular, we focus on a spectrum of impressive ML endeavors investigating the state-of-the-art thermal transport modeling (density functional theory, molecular dynamics, and Boltzmann transport equation), different families of materials (semiconductors, polymers, alloys, and composites), assorted aspects of thermal properties (conductivity, emissivity, stability, and thermoelectricity), and engineering prediction and optimization (devices and systems). We discuss the promises and challenges of current ML approaches and provide perspectives for future directions and new algorithms that could make further impacts on thermal energy research.

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“…There are different ways to enhance the performance of the techniques developed in [27]. To solve the main problem of the linear characteristics of the PCA in high-dimensional spaces, the kernel PCA (KPCA) will be applied to extract high-order statistical information in the DL parameter space.…”

Section: ) Enhance Decision Making In Deep Learningmentioning

confidence: 99%

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Mansouri¹,

Trabelsi

Nounou

et al. 2021

IEEE Access

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Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DLbased FDD for PV systems and provide guidelines for future research in the field.

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Interval-Valued Features Based Machine Learning Technique for Fault Detection and Diagnosis of Uncertain HVAC Systems

Cited by 18 publications

References 37 publications

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

A Review of Data-Driven Approaches and Techniques for Fault Detection and Diagnosis in HVAC Systems

Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

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