Cross Diversity Entropy-Based Feature Extraction for Fault Diagnosis of Rotor System

Li, Yongbo; Jiao, Zehang; Wang, Shun; Feng, Ke; Liu, Zheng

doi:10.1109/tmech.2023.3318633

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The current domain adaptation methods can be divided into two main categories: based on implicit distance [51,52] and those based on explicit distance [53][54][55]. The former use the perspective of feature separability by distinguishing the source of features to achieve the purpose of domain adaptation.…”

Section: Introductionmentioning

confidence: 99%

“…This helps the features extracted by the feature extractor maintain consistency between the two domains and enhances the generalization capacity of the model. The second group of methods directly use explicit distance to characterize the discrepancies in feature distribution between domains, which can be roughly divided into distance-based methods [53,54,58], similarity-based methods [55,59] and divergence-based methods [60,61]. The distance-based methods are mainly divided into kernel distance-based methods, such as maximum mean discrepancy (MMD) [53,62,63], and other methods, including correlation alignment (CORAL) [64,65] and Riemannian manifold [66,67].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

Zhou,

He,

Yang

et al. 2024

Meas. Sci. Technol.

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The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under variable working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network (CNN) and the graph convolutional network (GCN) to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of local neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under variable working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

Zhou,

He,

Yang

et al. 2024

Meas. Sci. Technol.

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A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy

Wang,

Yao,

et al. 2025

IEEE Trans. Instrum. Meas.

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Fractional Diversity Entropy: A Vibration Signal Measure to Assist a Diffusion Model in the Fault Diagnosis of Automotive Machines

Wang,

Zhang,

Wang

et al. 2024

Electronics

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Real-world vibration signal acquisition of automotive machines often results in imbalanced sample sets due to restricted test conditions, adversely impacting fault diagnostic accuracy. To address this problem, we propose fractional diversity entropy (FrDivEn) and incorporate it into the classifier-guided diffusion model (CGDM) to synthesize high-quality samples. Additionally, we present a corresponding imbalanced fault diagnostic method. This method first converts vibration data to Gramian angular field (GAF) image samples through GAF transformation. Then, FrDivEn is mapped to the gradient scale of CGDM to trade off the diversity and fidelity of synthetic samples. These synthetic samples are mixed with real samples to obtain a balanced sample set, which is fed to the fine-tuned pretrained ConvNeXt for fault diagnosis. Various sample synthesizers and fault classifiers were combined to conduct imbalanced fault diagnosis experiments across bearing, gearbox, and rotor datasets. The results indicate that for the three datasets, the diagnostic accuracies of the proposed CGDM using FrDivEn at an imbalance ratio of 40:1 are 91.22%, 87.90%, and 98.89%, respectively, which are 7.32%, 11.59%, and 3.48% higher than that of the Wasserstein generative adversarial network (WGAN), respectively. The experimental results across the three datasets validated the validity and generalizability of the proposed diagnostic method.

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Cross Diversity Entropy-Based Feature Extraction for Fault Diagnosis of Rotor System

Cited by 3 publications

References 52 publications

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

A High-Accuracy Fault Detection Method Using Swarm Intelligence Optimization Entropy

Fractional Diversity Entropy: A Vibration Signal Measure to Assist a Diffusion Model in the Fault Diagnosis of Automotive Machines

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