Big data analytics deep learning techniques and applications: A survey

Selmy, Hend A.; Mohamed, Hoda K.; Medhat, Walaa

doi:10.1016/j.is.2023.102318

Cited by 8 publications

(5 citation statements)

References 110 publications

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“…Deep Learning, as an important branch of artificial intelligence, is an advanced form of machine learning [14]. It attempts to simulate the working mode of human brain neurons, through building and training neural network model, to achieve automatic learning and understanding of data.…”

Section: Basic Concepts Of Deep Learningmentioning

confidence: 99%

Applications and Challenges of Deep Learning in Oil and Gas Field Development

Haohao,

Xiang,

Lei

et al. 2024

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Under the background of global energy transformation and environmental protection, the application of artificial intelligence technology has become an important trend in oil and gas field development industry. However, how to effectively utilize artificial intelligence technology to improve the efficiency and safety of oil and gas development, while addressing the environmental and economic issues it brings, is a major question that researchers need to consider. Based on the actual needs of oil and gas exploitation, the basic principles and methods of deep learning are studied, and the main models and training methods of deep learning are introduced. The basic process of oil and gas field development is described in detail, and the realization steps and principles of depth learning optimization model for oil and gas field development are studied. The main challenges of depth learning in oil and gas field development are studied, including data security, model complexity, computing resource demand and so on. The results show that as a powerful artificial intelligence tool, deep learning has great potential to improve the efficiency and security of oil and gas exploitation, but it still faces some challenges. Therefore, future research should pay more attention to these problems to promote the application of deep learning in oil and gas development.

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Section: Basic Concepts Of Deep Learningmentioning

confidence: 99%

Applications and Challenges of Deep Learning in Oil and Gas Field Development

Haohao,

Xiang,

Lei

et al. 2024

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“…DL techniques, especially Autoencoders, are utilized for their proficiency in data compression and feature extraction from high-dimensional data [66], [67]. Autoencoders can transform complex datasets into manageable forms without losing critical information, facilitating more accurate fault diagnosis and system health assessments [68].…”

Section: Literature Reviewmentioning

confidence: 99%

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Baroud

2024

J. Appl. Data Sci.

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Integrating Artificial Intelligence (AI) within Industry 4.0 has propelled the evolution of fault diagnosis and predictive maintenance (PdM) strategies, marking a significant shift towards smarter maintenance paradigms in the mechatronics sector. With the advent of Industry 4.0, mechatronic systems have become increasingly sophisticated, highlighting the critical need for advanced maintenance methodologies that are both efficient and effective. This paper delves into the confluence of cutting-edge AI techniques, including machine learning (ML) and deep learning (DL), with multi-agent systems (MAS) to enhance fault diagnosis precision and facilitate PdM in the context of Industry 4.0. Specifically, we explore the use of various ML models, including Support Vector Machines (SVMs) and Random Forests (RFs), and DL architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), which have been effectively oriented to analyses complex industrial data. Initially, the study examines the progress in AI algorithms that accelerate fault identification by leveraging data from system operations, sensors, and historical trends. AI-enabled fault diagnosis rapidly detects irregularities and discerns the fundamental causes, thereby minimizing downtime and enhancing system reliability and efficiency. Furthermore, this paper underscores the adoption of AI-driven PdM approaches, emphasizing prognostics that predict the Remaining Useful Life (RUL) of machinery. This predictive capability allows for the strategic scheduling of maintenance activities, optimizing resource use, prolonging the lifespan of expensive assets, and refining the management of spare parts inventory. The tangible advantages of employing AI for fault diagnosis and PdM are showcased through a case study from authentic mechatronics implementations. This case study highlights successful implementations, documenting real-world challenges such as data integration issues and system interoperability, and elaborates on the strategies deployed to navigate these obstacles. The results demonstrate improved operational reliability and cost savings and shed light on the pragmatic considerations and solutions that facilitate the adoption of AI and MAS in industrial applications. The paper also navigates the challenges and prospective research avenues in applying AI within the mechatronics domain of Industry 4.0, setting the stage for ongoing innovation and exploration in this transformative domain.

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“…Data-driven modeling techniques have been widely used in the industry after decades of development, mainly to solve modeling problems related to key indicators in industrial processes [ 7 , 8 , 9 ]. These techniques include Support Vector Machines, partial least squares, neural networks, and deep learning networks.…”

Section: Introductionmentioning

confidence: 99%

Research on Classification Algorithm of Silicon Single-Crystal Growth Temperature Gradient Trend Based on Multi-Level Feature Fusion

Liu,

Mu,

Zhang

et al. 2024

Sensors

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In the process of silicon single-crystal preparation, the timely identification and adjustment of abnormal conditions are crucial. Failure to promptly detect and resolve issues may result in a substandard silicon crystal product quality or even crystal pulling failure. Therefore, the early identification of abnormal furnace conditions is essential for ensuring the preparation of perfect silicon single crystals. Additionally, since the thermal field is the fundamental driving force for stable crystal growth and the primary assurance of crystal quality, this paper proposes a silicon single-crystal growth temperature gradient trend classification algorithm based on multi-level feature fusion. The aim is to accurately identify temperature gradient changes during silicon crystal growth, in order to promptly react to early growth failures and ensure the stable growth of high-quality silicon single crystals to meet industrial production requirements. The algorithm first divides the temperature gradient trend into reasonable categories based on expert knowledge and qualitative analysis methods. Then, it fuses the original features of actual production data, shallow features extracted based on statistical information, and deep features extracted through deep learning. During the fusion process, the algorithm considers the impact of different features on the target variable and calculates mutual information based on the difference between information entropy and conditional entropy, ultimately using mutual information for feature weighting. Subsequently, the fused multi-level feature vectors and their corresponding trend labels are input into a Deep Belief Network (DBN) model to capture process dynamics and classify trend changes. Finally, the experimental results demonstrate that the proposed algorithm can effectively predict the changing trend of thermal field temperature gradients. The introduction of this algorithm will help improve the accuracy of fault trend prediction in silicon single-crystal preparation, thereby minimizing product quality issues and production interruptions caused by abnormal conditions.

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Big data analytics deep learning techniques and applications: A survey

Cited by 8 publications

References 110 publications

Applications and Challenges of Deep Learning in Oil and Gas Field Development

Applications and Challenges of Deep Learning in Oil and Gas Field Development

Cutting-Edge AI Approaches with MAS for PdM in Industry 4.0: Challenges and Future Directions

Research on Classification Algorithm of Silicon Single-Crystal Growth Temperature Gradient Trend Based on Multi-Level Feature Fusion

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