A Novel Hybrid Artificial Bee Colony-Based Deep Convolutional Neural Network to Improve the Detection Performance of Backscatter Communication Systems

Electronics, Год журнала: 2023, Номер 12(10), С. 2263 - 2263

Опубликована: Май 16, 2023

Backscatter communication (BC) is a promising technology for low-power and low-data-rate applications, though the signal detection performance limited since backscattered usually much weaker than original signal. When poor, backscatter device (BD) may not be able to accurately detect interpret incoming signal, leading errors degraded quality. This can result in data loss, slow transfer rates, reduced reliability of link. paper proposes novel approach improve systems using evolutionary deep learning. In particular, we focus on training convolutional neural networks (DCNNs) BC. We first develop hybrid algorithm based artificial bee colony (ABC), biogeography-based optimization (BBO), particle swarm (PSO) optimize architecture DCNN, followed by large set benchmark datasets. To ABC, migration operator BBO used exploitation. Moving towards global best PSO also proposed exploration ABC. Then, take advantage bit-error rate (BER) studied BC system. The simulation results demonstrate that has show significantly improves signals compared existing works.

Язык: Английский

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Pseudo-Label Guided Sparse Deep Belief Network Learning Method for Fault Diagnosis of Radar Critical Components

IEEE Transactions on Instrumentation and Measurement, Год журнала: 2023, Номер 72, С. 1 - 12

Опубликована: Янв. 1, 2023

Effective fault diagnosis of critical components is essential to ensure the safe and reliable operation entire system. This paper deals with transmitter/receiver module, which a component in phased array radar system, by proposing novel deep belief network learning method. A sparse based on Gaussian function first constructed automatically learn relationship between monitoring data health conditions. With trained network, pseudo-labels are produced for unlabeled samples, while information entropy employed calculate confidence levels reflecting their certainty reduce effect pseudo-label noise. The pseudo-labeled samples high added training set retrain network. Optimal model configuration parameters obtained through chaos game optimization algorithm. effectiveness proposed method verified real-world dataset from certain type radar. experiments show that mean identification rate this can reach 96.33%, not only exceeds some network-based modeling methods, but also other intelligent methods.

Язык: Английский

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Deep Learning in Industrial Machinery: A Critical Review of Bearing Fault Classification Methods

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112785 - 112785

Опубликована: Янв. 1, 2025

Язык: Английский

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Fault Diagnosis of Bearings Using Wavelet Packet Energy Spectrum and SSA-DBN

Processes, Год журнала: 2023, Номер 11(7), С. 1875 - 1875

Опубликована: Июнь 22, 2023

To enhance fault characteristics and improve detection accuracy in bearing vibration signals, this paper proposes a diagnosis method using wavelet packet energy spectrum an improved deep confidence network. Firstly, transform decomposes the original signal into different frequency bands, fully preserving signal’s information, constructs feature vectors by extracting of sub-frequency bands via to extract information. Secondly, minimize time-consuming manual parameter adjustment procedure increase diagnostic accuracy, sparrow search algorithm–deep belief network is proposed, which utilizes algorithm optimize hyperparameters networks reduce classification error rate. Finally, verify effectiveness method, rolling data from Casey Reserve University were selected for verification, compared other commonly used algorithms, proposed achieved 100% 99.34% two sets comparative experiments. The experimental results demonstrate that has high rate stability.

Язык: Английский

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A Hybrid Method for Fault Diagnosis of Rolling Bearings

Measurement Science and Technology, Год журнала: 2024, Номер 35(12), С. 125012 - 125012

Опубликована: Сен. 4, 2024

Abstract Traditional diagnostic methods often have insufficient accuracy and noise reduction, which leads to errors. To address these issues, this paper proposes an advanced fault diagnosis model that combines the variational mode decomposition (VMD) improved by a Variable-Objective Search Whale Optimization Algorithm (VSWOA) with Pelican (PO)-boosted Kernel Extreme Learning Machine (KELM) algorithm. The application of method is shown here in rolling bearings. proposed VSWOA enhances performance VMD incorporating Sobol sequence, nonlinear time-varying factors, multi-objective initial search strategy, elite Cauchy chaos mutation significantly improving reduction vibration signals. Fault information precisely extracted using waveform sample entropy, composite multiscale fuzzy enables effective feature screening dimensionality reduction. POA fine-tunes KELM parameters, increasing classification accuracy. effectiveness verified through experimental evaluations bearing data injected Gaussian (from Case Western Reserve University) SpectraQuest datasets, where significant improvements detection are achieved.

Язык: Английский

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Triple feature extraction method based on multi-scale dispersion entropy and multi-scale permutation entropy in sound-based fault diagnosis

Frontiers in Physics, Год журнала: 2023, Номер 11

Опубликована: Апрель 4, 2023

Fault of rolling bearing signal is a common problem encountered in the production life. Identifying fault helps to locate location and type quickly, react time, reduce losses caused by failure production. In order accurately identify signal, this paper presents triple feature extraction classification method based on multi-scale dispersion entropy (MDE) permutation (MPE), extracts features when it working, uses algorithm determine whether there fault. Scale 2 MDE combined with scale 1 MPE as three required for experiment. As comparison recognition results, (MSE)is introduced. Ten scales are calculated, all combinations obtained. K nearest neighbor used recognition. The result shows that combination rate proposed reaches 96.2%, which best among combinations.

Язык: Английский

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High-low frequency features fusion and integrated classification SCNs for intelligent fault diagnosis of rolling bearing

International Journal of Machine Learning and Cybernetics, Год журнала: 2024, Номер unknown

Опубликована: Окт. 4, 2024

Язык: Английский

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Fault Diagnosis of Balancing Machine Based on ISSA-ELM

Computational Intelligence and Neuroscience, Год журнала: 2022, Номер 2022, С. 1 - 11

Опубликована: Окт. 15, 2022

Balancing machine is a general equipment for dynamic balance verification of rotating parts, whether it breaks down or does not determine the accuracy verification. In order to solve problem insufficient fault diagnosis balancing machine, method based on Improved Sparrow Search Algorithm (ISSA) optimized Extreme Learning Machine (ELM) was proposed. Firstly, iterative chaos mapping and Fuch were introduced initialize population increase diversity. Secondly, adaptive factor Levy flight strategy also update individual positions improve model convergence speed. Finally, feature vector input ISSA-ELM with type as output. The experiment showed that high 99.17%, which 1.67%, 2.50%, 7.50%, 17.50% higher than SSA-ELM, HHO-ELM, PSO-ELM, ELM, respectively, further improving prediction operation state machine.

Язык: Английский

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Advanced Machine Learning Applications in Big Data Analytics

Electronics, Год журнала: 2023, Номер 12(13), С. 2940 - 2940

Опубликована: Июль 4, 2023

We are currently living in the era of big data. [...]

Язык: Английский

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Application of Sparse Deep Belief Network Optimized by Adaptive Sine Cosine Algorithm in Fault Diagnosis of Radar Transceivers

2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS), Год журнала: 2023, Номер unknown, С. 1 - 6

Опубликована: Сен. 22, 2023

Transceiver is one of the most important components radar, and its efficiency greatly affects reliability entire radar system. To improve accuracy fault diagnosis for transceivers, a sparse deep belief network (DBN)-based diagnostic model that uses adaptive sine cosine algorithm (SCA) optimization proposed. Specifically, regular term added to DBN loss function prevent overfitting in training. At same time, an strategy studied realize autonomous switching SCA, so as precisely optimize hyper-parameters. Therefore, fusion SCA forms effective model, named ASCA-SDBN. The efficacy proposed ASCA-SDBN validated using real-world dataset from transceivers. Experimental results show this outperforms several popular intelligent models.

Язык: Английский

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