A novel multi-classifier information fusion based on Dempster–Shafer theory: application to vibration-based fault detection

Yaghoubi, Vahid; Cheng, Liangliang; Paepegem, Wim Van; Kersemans, Mathias

doi:10.1177/14759217211007130

Cited by 19 publications

(19 citation statements)

References 35 publications

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“…For this purpose, a variety of techniques have been proposed in the literature. 36,48,49 In this study, it is proposed to employ generative functions to extract information in the FRFs at different scales. That is, the FRFs are first passed through several generative functions and then used to train the CNNs.…”

Section: Proposed Methodology: Cnn-dstmentioning

confidence: 99%

“…This is an improvement to the method proposed in Ref. 36 In Ref. 36, the credibility of the evidences is used as a weight to modify them before the combination.…”

Section: Proposed Methodology: Cnn-dstmentioning

confidence: 99%

“…36 In Ref. 36, the credibility of the evidences is used as a weight to modify them before the combination. In the current study, the support degree of the evidences with respect to the focal elements with the largest BBAs is also included in the weight, which could be beneficial in reducing the conflict.…”

Section: Proposed Methodology: Cnn-dstmentioning

confidence: 99%

“…For instance, in Refs. 22, 36, and 37, it is shown that the DST-based ensemble model could outperform the best individual model. However, when conflicting evidences come from different models, it could lead to counter-intuitive results.…”

Section: Introductionmentioning

confidence: 99%

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CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

Yaghoubi

Cheng

Paepegem

et al. 2022

Structural Health Monitoring

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Nowadays, using vibration data in conjunction with pattern recognition methods is one of the most common fault detection strategies for structures. However, their performances depend on the features extracted from vibration data, the features selected to train the classifier, and the classifier used for pattern recognition. Deep learning facilitates the fault detection procedure by automating the feature extraction and selection, and classification procedure. Though, deep learning approaches have challenges in designing its structure and tuning its hyperparameters, which may result in a low generalization capability. Therefore, this study proposes an ensemble deep learning framework based on a convolutional neural network (CNN) and Dempster–Shafer theory (DST), called CNN-DST. In this framework, several CNNs with the proposed structure are first trained, and then, the outputs of the CNNs selected by the proposed technique are combined by using an improved DST-based method. To validate the proposed CNN-DST framework, it is applied to an experimental dataset created by the broadband vibrational responses of polycrystalline nickel alloy first-stage turbine blades with different types and severities of damage. Through statistical analysis, it is shown that the proposed CNN-DST framework classifies the turbine blades with an average prediction accuracy of 97.19%. The proposed CNN-DST framework is benchmarked with other state-of-the-art classification methods, demonstrating its high performance. The robustness of the proposed CNN-DST framework with respect to measurement noise is investigated, showing its high noise-resistance. Further, bandwidth analysis reveals that most of the required information for detecting faulty samples is available in a small frequency range.

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Section: Proposed Methodology: Cnn-dstmentioning

confidence: 99%

“…This is an improvement to the method proposed in Ref. 36 In Ref. 36, the credibility of the evidences is used as a weight to modify them before the combination.…”

Section: Proposed Methodology: Cnn-dstmentioning

confidence: 99%

Section: Proposed Methodology: Cnn-dstmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

Yaghoubi

Cheng

Paepegem

et al. 2022

Structural Health Monitoring

Self Cite

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“…It meets the requirement of traditional algorithms for a priori probability and provides a basis for event decisionmaking. Its typical features make it widely used in fault diagnosis, anomaly detection, reliability, inference, prognosis, and early prediction [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

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Offshore Wind‐Hydrogen Systems Fault Detection Based on CNN‐BiLSTM‐AM Algorithm

Zhao,

Sun,

Zhou

et al. 2024

Quality & Reliability Eng

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This study presents a novel deep learning‐based approach for fault detection in offshore wind‐hydrogen systems. A fault detection model is developed using convolutional neural networks (CNNs), bidirectional long short‐term memory networks (BiLSTMs), and an attention mechanism (AM). The model is trained on a dataset generated through fault injection techniques, which simulate real‐world faults in the system. Key operational parameters, such as wind speed and hydrogen production rate, are used to detect faults effectively. This paper reduces reliance on actual experiments, and introducing artificial faults allows system performance assessment under different fault scenarios, lowering project risks and costs. This work facilitates automatic feature extraction and high‐precision classification of time‐series fault data, which covers a fully automated learning process from data to fault detection. The outstanding performance of the method is validated through computation and result comparison.

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A novel multi-classifier information fusion based on Dempster–Shafer theory: application to vibration-based fault detection

Cited by 19 publications

References 35 publications

CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

CNN-DST: Ensemble deep learning based on Dempster–Shafer theory for vibration-based fault recognition

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Offshore Wind‐Hydrogen Systems Fault Detection Based on CNN‐BiLSTM‐AM Algorithm

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