Fault recognition of large-size low-speed slewing bearing based on improved deep belief network

Pan, Yubin; Wang, Hua; Chen, Jie; Hong, Rongjing

doi:10.1177/10775463221085856

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…DBN is a dual-method deep network and it can be the structure of the RBM model. The trained method for DBM encompasses finetuned, pre-trained, and prediction [35]. DBN is a propagative graphical model.…”

Section: B Prc Classification Using the Lstm-dbn Modelmentioning

confidence: 99%

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Sethi,

Singh,

Rout

et al. 2024

IEEE Access

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Prostate cancer (PRC) is the major reason of mortality globally. Early recognition and classification of PRC become essential to enhance the quality of healthcare services. A newly established deep learning (DL) and machine learning (ML) approach with different optimization tools can be employed to classify accurately of PRC accurately using microarray gene expression data (GED). Though the microarray data structures are important to diagnosing different kinds of diseases, the optimum hyperparameter tuning of the DL models poses a major challenge to achieving maximum classification performance. To resolve these issues, this study develops a new Gene Expression Data Analysis using Artificial Intelligence for Prostate Cancer Diagnoses (GEDAAI-PCD) technique. The proposed GEDAAI-PCD technique examines the GED for the identification of PRC. To accomplish this, the GEDAAI-PCD technique initially normalizes the GED into a uniform format. In addition, the long short-term memory-deep belief network (LSTM-DBN) model was applied for PRC classification purposes. The wild horse optimization (EWHO) system was utilized as a hyperparameter tuning strategy to optimize the performance of the LSTM-DBN model. The experimental assessment of the GEDAAI-PCD system occurs on open openaccessed gene expression database. The experimental outcomes emphasized the supremacy of the GEDAAI-PCD method on PRC classification.

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Section: B Prc Classification Using the Lstm-dbn Modelmentioning

confidence: 99%

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Sethi,

Singh,

Rout

et al. 2024

IEEE Access

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“…Pan et al [124] presented a fault recognition method to be used for further SRA based on an improved DBN using the sampling method of free energy in persistent contrastive divergence (FEPCD). A systematic methodology based on multi-domain feature extraction is used to describe the characteristic fault information.…”

Section: Dbnmentioning

confidence: 99%

Deep learning-based methods in structural reliability analysis: a review

Afshari

Zhao

Zhuang

et al. 2023

Meas. Sci. Technol.

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One of the most significant and growing research fields in mechanical and civil engineering is Structural Reliability Analysis (SRA). A reliable and precise SRA usually has to deal with complicated , aand numerically expensive problems. Artificial intelligence-based (AI) nd specifically, Deep learning-based (DL) methods, have been applied to the SRA problems to reduce the computational cost and to improve the accuracy of reliability estimation as well. This article reviews the recent advances in using DL models in SRA problems. The review includes the most common categories of DL-based methods used in SRA. More specifically, the application of supervised methods, unsupervised methods, and hybrid deep learning methods in SRA are explained. In this paper, the supervised methods for SRA are categorized as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN ), Long short-term memory (LSTM), Bidirectional (Bi-LSTM) and Gated recurrent units (GRU). For the unsupervised methods, we have investigated methods such as Generative Adversarial Network (GAN), Autoencoders (AE), Self-Organizing Map (SOM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We have made a comprehensive survey of these methods in SRA. Aiming towards an efficient SRA, deep learning-based methods applied for approximating the limit state function (LSF) with First/Second Order Reliability Methods (FORM/SORM), Monte Carlo simulation (MCS), or MCS with importance sampling (IS). Accordingly, the current paper focuses on the structure of different DL-based models and the applications of each DL method in various SRA problems. This survey helps researchers in mechanical and civil engineering, especially those who are engaged with structural and reliability analysis or dealing with quality assurance problems.

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“…DT technologies [1-3], the internet of things [4], cloud computing [5], and artificial intelligence [6] all have the potential to revolutionize the way bearing faults are presented in turbines. The majority of the current bearing fault diagnosis research uses data-driven methods, such as signal analysis and machine learning [7], to identify bearing faults. The area of fault diagnosis makes extensive use of deep learning due to its powerful feature extraction capabilities.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

Islam,

Sarwar,

Hamza

et al. 2024

ejaset

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This study presents a digital twin (DT) based wind turbine bearing fault diagnosis approach to address the issues of insufficient fault sample size and inaccurate diagnosis. To assist in diagnosing bearing faults in wind turbines, a DT system was built. Bearing vibration signal enhancement processing, which is based on the Hilbert-Huang transform, is used to improve the data samples of vibration signals and decrease the noise in these signals. In order to diagnose bearing defects in wind turbines, a convolutional neural network model was trained and tested using data-enhanced samples. The experimental results showed that the suggested method is feasible and effective, increased the stability and accuracy of defect diagnosis in wind turbine bearings, and solved the problem of data augmentation in one-dimensional vibration signals.

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Fault recognition of large-size low-speed slewing bearing based on improved deep belief network

Cited by 13 publications

References 32 publications

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification

Deep learning-based methods in structural reliability analysis: a review

A Deep Learning Fault Diagnose Method for Turbine Bearing: Digital Twin Mechanism

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