Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review

Neural Computing and Applications, Год журнала: 2022, Номер 35(1), С. 211 - 252

Опубликована: Ноя. 18, 2022

Язык: Английский

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RCA: YOLOv8-Based Surface Defects Detection on the Inner Wall of Cylindrical High-Precision Parts

Arabian Journal for Science and Engineering, Год журнала: 2024, Номер 49(9), С. 12771 - 12789

Опубликована: Март 12, 2024

Язык: Английский

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Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks

Energies, Год журнала: 2023, Номер 16(22), С. 7680 - 7680

Опубликована: Ноя. 20, 2023

Effective fault detection, classification, and localization are vital for smart grid self-healing mitigation. Deep learning has the capability to autonomously extract characteristics discern categories from three-phase raw of voltage current signals. With rise distributed generators, conventional relaying devices face challenges in managing dynamic currents. Various deep neural network algorithms have been proposed location. This study introduces innovative detection methods using Artificial Neural Networks (ANNs) one-dimension Convolution (1D-CNNs). Leveraging sensor data such as measurements, our approach outperforms contemporary terms accuracy efficiency. Results IEEE 6-bus system showcase impressive rates: 99.99%, 99.98% identifying faulty lines, 99.75%, 99.99% 98.25%, 96.85% location ANN 1D-CNN, respectively. emerges a promising tool enhancing classification within grids, offering significant performance improvements.

Язык: Английский

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Automatic fault detection in grid-connected photovoltaic systems via variational autoencoder-based monitoring

Energy Conversion and Management, Год журнала: 2024, Номер 314, С. 118665 - 118665

Опубликована: Июнь 21, 2024

Язык: Английский

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Prognostics and health management of photovoltaic systems based on deep learning: A state-of-the-art review and future perspectives

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 205, С. 114861 - 114861

Опубликована: Авг. 26, 2024

Язык: Английский

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Explainable deep learning method for power system stability evaluation with incomplete voltage data based on transfer learning

Measurement, Год журнала: 2025, Номер unknown, С. 116781 - 116781

Опубликована: Янв. 1, 2025

Язык: Английский

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Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review

Energies, Год журнала: 2023, Номер 16(21), С. 7417 - 7417

Опубликована: Ноя. 3, 2023

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up 10%. The efficiency systems depends upon the reliable and diagnosis faults. integration Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. goal this systematic review offer comprehensive overview recent advancements AI-based methodologies for detection, consolidating key findings from 31 research papers. An initial pool 142 papers were identified, which selected in-depth following PRISMA guidelines. title, objective, methods, each paper analyzed, focus on machine learning (ML) deep (DL) approaches. ML DL are particularly suitable their capacity process analyze large amounts data identify complex patterns anomalies. This study identified several AI used systems, ranging classical methods like k-nearest neighbor (KNN) random forest more advanced models such as Convolutional Neural Networks (CNNs). Quantum circuits infrared imagery also explored potential solutions. analysis found that models, general, outperformed traditional accuracy efficiency. shows have evolved increasingly applied detection. offers high effectiveness. After reviewing studies, we proposed an Network (ANN)-based method classification.

Язык: Английский

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Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers

IEEE Open Journal of the Industrial Electronics Society, Год журнала: 2023, Номер 4, С. 230 - 241

Опубликована: Янв. 1, 2023

This paper proposes a digital twin-based diagnosis and fault tolerant control for T-type three-level rectifiers. To develop the twin (DT), dense deep neural network (DNN) machine learning approach is used. The trained offline using set of experimental data updated online to get maximum possible accuracy. Then, DT used tolerance open-switch faults (OSFs) related voltage current sensors (VACSF) or sensorless control. detection localization algorithm implemented based on dynamic response difference between physical system its twin. First, detected localized grid dynamics, where each switch generates specific pattern in dynamics. Open-switch tolerated by changing switching function location fault. Second, when provides amplitude currents while do not provide correct measurement. case feeding back voltages from as an alternative sensors. proposed technique has low overhead, enhances reliability power converter, applicable mode Experimental investigations are conducted validate concept.

Язык: Английский

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Dynamic Modeling for Fault Diagnosis in PV Systems Utilizing AI Techniques Based on Multilayer Perceptron (MLP)

Опубликована: Янв. 28, 2024

This paper introduces an advanced fault diagnosis methodology for Solar Photovoltaic (PV) systems, employing Multilayer Perceptron (MLP) neural networks driven by Artificial Intelligence (AI) techniques. The critical task of is addressed through the effective utilization historical PV array data. developed MLP framework exhibits exceptional accuracy in detecting common faults, particularly module degradation, crucial ensuring sustained system integrity and performance. research involves meticulous creation detailed model parameters, enabling precise simulation behavior under varying conditions. Leveraging data MLP-based training, approach adeptly captures diverse states, facilitating accurate simulations reflective real-world scenarios. Furthermore, integration techniques enhances model's efficiency classifying thereby timely maintenance interventions. To underscore its effectiveness, we conducted a comprehensive comparison between our method prior AI technique. comparative analysis highlights superior performance robustness proposed detection diagnosis. Validation extensive showcases remarkable predicting diagnosing faults. findings significant contribution this AI-driven to research, providing robust essential reliability optimizing

Язык: Английский

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Object Detection in Autonomous Vehicles: A Performance Analysis

Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 277 - 291

Опубликована: Янв. 1, 2024

Язык: Английский

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