Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks DOI Creative Commons
Zhaolin Wang, Zhiping Zhang

EAI Endorsed Transactions on Energy Web, Год журнала: 2025, Номер 12

Опубликована: Май 2, 2025

INTRODUCTION: High energy consuming enterprises continue to pay increasing attention consumption. Therefore, designing an management system is significant.OBJECTIVES: To improve the level and economic benefits of enterprises, a high enterprise design based on Internet Things technology neural network algorithms proposed.METHODS: devices are used for data collection transmission. The combination model prediction optimization can achieve real-time monitoring, prediction, control consumption.RESULTS: research results indicated that response time proposed in study was 80.2 ms when number people 600. fluctuation range CPU usage within 24 hours 14% 45%.CONCLUSION: A integrates networks manage more efficiently intelligently, thereby improving production efficiency enterprise. This helps companies gain greater advantages fierce market competition.

Язык: Английский

Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing DOI
Lingli Cui, Yongchang Xiao, Dongdong Liu

и другие.

Reliability Engineering & System Safety, Год журнала: 2024, Номер 245, С. 109991 - 109991

Опубликована: Фев. 5, 2024

Язык: Английский

Процитировано

56

The LPST-Net: A new deep interval health monitoring and prediction framework for bearing-rotor systems under complex operating conditions DOI
Tongguang Yang, Guanchen Li, Kaitai Li

и другие.

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102558 - 102558

Опубликована: Апрель 22, 2024

Язык: Английский

Процитировано

19

Multi-Scale Analysis of Knee Joint Acoustic Signals for Cartilage Degeneration Assessment DOI Creative Commons
Anna Machrowska, Robert Karpiński, Marcin Maciejewski

и другие.

Sensors, Год журнала: 2025, Номер 25(3), С. 706 - 706

Опубликована: Янв. 24, 2025

This study focuses on the diagnostic analysis of cartilage damage in knee joint based acoustic signals generated by joint. The research utilizes a combination advanced signal processing techniques, specifically empirical mode decomposition (EEMD) and detrended fluctuation (DFA), alongside convolutional neural networks (CNNs) for classification detection tasks. Acoustic signals, often reflecting mechanical behavior during movement, serve as non-invasive tool assessing condition. EEMD is applied to decompose into intrinsic functions (IMFs), which are then analyzed using DFA quantify scaling properties detect irregularities indicative damage. separation individual frequency components allows multi-scale with each resulting from local variations amplitude over time allowing effective removal noise present signal. CNN model trained features extracted these accurately classify different stages degeneration. proposed method demonstrates potential early pathology, providing valuable preventive healthcare reducing need invasive procedures. results suggest that EEMD-DFA feature extraction offers promising approach assessment

Язык: Английский

Процитировано

4

Artificial intelligence-based data-driven prognostics in industry: A survey DOI
Mohamed A. El-Brawany,

Dina A. Ibrahim,

Hamdy K. Elminir

и другие.

Computers & Industrial Engineering, Год журнала: 2023, Номер 184, С. 109605 - 109605

Опубликована: Сен. 9, 2023

Язык: Английский

Процитировано

23

Intelligent Fault Detection and Classification Schemes for Smart Grids Based on Deep Neural Networks DOI Creative Commons
Ahmed Sami Alhanaf, Hasan H. Balık, Murtaza Farsadi

и другие.

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

Процитировано

18

A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance DOI
Suraj Gupta, Akhilesh Kumar, J. Maiti

и другие.

Safety Science, Год журнала: 2024, Номер 177, С. 106590 - 106590

Опубликована: Июнь 17, 2024

Язык: Английский

Процитировано

9

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model DOI Creative Commons
Yu Wang, Dexiong Li, Lei Li

и другие.

Heliyon, Год журнала: 2024, Номер 10(15), С. e35407 - e35407

Опубликована: Июль 30, 2024

In the context of burgeoning industrial advancement, there is an increasing trend towards integration intelligence and precision in mechanical equipment. Central to functionality such equipment rolling bearing, whose operational integrity significantly impacts overall performance machinery. This underscores imperative for reliable fault diagnosis mechanisms continuous monitoring bearing conditions within production environments. Vibration signals are primarily used because they provide comprehensive information about equipment's condition. However, data often contain high noise levels, high-frequency variations, irregularities, along with a significant amount redundant information, like duplication, overlap, unnecessary during signal transmission. These characteristics present considerable challenges effective feature extraction diagnosis, reducing accuracy reliability traditional detection methods. research introduces innovative methodology bearings using deep convolutional neural networks (CNNs) enhanced variational autoencoders (VAEs). learning approach aims precisely identify classify faults by extracting detailed vibration features. The VAE enhances robustness, while CNN improves expressiveness, addressing issues gradient vanishing explosion. model employs reparameterization trick unsupervised latent features further trains CNN. system incorporates adaptive threshold methods, "3/5" strategy, Dropout VAE-CNN different types at rotational speeds typically reaches more than 90 %, it achieves generally acceptable result. Meanwhile, augmented model, after experimental validation various dimensions, can achieve satisfactory results compared several representative network models without augmentation, improving robustness diagnosis.

Язык: Английский

Процитировано

9

Comprehensive analysis of change-point dynamics detection in time series data: A review DOI
Muktesh Gupta, Rajesh Wadhvani, Akhtar Rasool

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 248, С. 123342 - 123342

Опубликована: Янв. 29, 2024

Язык: Английский

Процитировано

8

An autonomous recognition framework based on reinforced adversarial open set algorithm for compound fault of mechanical equipment DOI
Zisheng Wang, Jianping Xuan, Tielin Shi

и другие.

Mechanical Systems and Signal Processing, Год журнала: 2024, Номер 219, С. 111596 - 111596

Опубликована: Июнь 12, 2024

Язык: Английский

Процитировано

8

Improved Marine Predators Algorithm and Extreme Gradient Boosting (XGBoost) for shipment status time prediction DOI
Resul Özdemir, Murat Taşyürek, Veysel Aslantaş

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 294, С. 111775 - 111775

Опубликована: Апрель 9, 2024

Язык: Английский

Процитировано

7