Bearing Fault Diagnosis Based on Multisensor Information Coupling and Attentional Feature Fusion

Wan, Shaoke; Li, Tianqi; Fang, Bin; Yan, Ke; Hong, Jun; Li, Xiaohu

doi:10.1109/tim.2023.3269115

Cited by 13 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secondly, integrating multiple attention mechanisms improves the diversity of the model, diverse information processing methods help to improve the robustness and generalization ability of the model. Finally, fusing multiple attention mechanisms has flexibility in that it could be flexibly combined according to specific tasks and data characteristics to adapt to different application scenarios ( Wan et al., 2023 ). Therefore, in this paper, a fused attention mechanism was added to the citrus young fruit detection model to improve the detection accuracy of the model.…”

Section: Experiments and Methodsmentioning

confidence: 99%

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Ang,

Zhiwei,

Wei

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

In order to address the challenges of inefficiency and insufficient accuracy in the manual identification of young citrus fruits during thinning processes, this study proposes a detection methodology using the you only look once for complex backgrounds of young citrus fruits (YCCB-YOLO) approach. The method first constructs a dataset containing images of young citrus fruits in a real orchard environment. To improve the detection accuracy while maintaining the computational efficiency, the study reconstructs the detection head and backbone network using pointwise convolution (PWonv) lightweight network, which reduces the complexity of the model without affecting the performance. In addition, the ability of the model to accurately detect young citrus fruits in complex backgrounds is enhanced by integrating the fusion attention mechanism. Meanwhile, the simplified spatial pyramid pooling fast-large kernel separated attention (SimSPPF-LSKA) feature pyramid was introduced to further enhance the multi-feature extraction capability of the model. Finally, the Adam optimization function was used to strengthen the nonlinear representation and feature extraction ability of the model. The experimental results show that the model achieves 91.79% precision (P), 92.75% recall (R), and 97.32% mean average precision (mAP)on the test set, which were improved by 1.33%, 2.24%, and 1.73%, respectively, compared with the original model, and the size of the model is only 5.4 MB. This study could meet the performance requirements for citrus fruit identification, which provides technical support for fruit thinning.

show abstract

Section: Experiments and Methodsmentioning

confidence: 99%

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Ang,

Zhiwei,

Wei

et al. 2024

Front. Plant Sci.

View full text Add to dashboard Cite

show abstract

“…This effectively realizes intelligent detection of forging defects. Wan et al [36] proposed an information coupling network that can independently and simultaneously extract and fuse features from different levels. Pan et al [37] utilized the MMD to measure the differences between different sensor data, proposing a joint distribution adaptation transfer learning strategy.…”

Section: Feature-level Fusionmentioning

confidence: 99%

A novel multi-sensor hybrid fusion framework

Du,

Wang,

Zhang

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Multi-sensor data fusion has emerged as a powerful approach to enhance the accuracy and robustness of diagnostic systems. However, effectively integrating multiple sensor data remains a challenge. To address this issue, this paper proposes a novel multi-sensor fusion framework. Firstly, a vibration signal weighted fusion rule based on Kullback-Leibler (K-L) Divergence-Permutation Entropy (PE) is introduced, which adaptively determines the weighting coefficients by considering the positional differences of different sensors. Secondly, a lightweight multi-scale convolutional neural network is designed for feature extraction and fusion of multi-sensor data. An ensemble classifier is employed for fault classification, and an improved hard voting strategy is proposed to achieve more reliable decision fusion. Finally, the superiority of the proposed method is validated using modular state detection data from the Kaggle database.

show abstract

“…S. Wan et al [1] introduced an approach to bearing fault detection with their multisensor information coupling network (MICN), which processes signals from various sensors to extract in-depth features independently and fuse them layer by layer. The proposed model features a novel feature-level information coupling technique, which utilizes a mutual attention mechanism during the multi-layer feature fusion process.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The outputs of the two GRUs with opposite directions jointly determine the output at the current position. The update gate output, denoted as 'z', is calculated using the following Equation (1).…”

Section: Bigru Modelmentioning

confidence: 99%

An MTBWO Algorithm Based on BiGRU Model

Yang,

Sun,

Zhang

2024

Electronics

View full text Add to dashboard Cite

To address the challenge of distinguishing the health status of bearings, in this paper, a health index (HI) is developed through utilization of the multiple target time-varying black widow optimization–bidirectional gating recurrent unit (MTBWO-BiGRU) model and the Bray–Curtis distance. This index offers a visual representation of the health status of bearings, enabling more intuitive monitoring and prediction. The first step involves utilizing L1 regularization to extract effective features as degradation elements from the current bearing vibration data. Additionally, the characteristics of the initial time window of the vibration data serve as the health features. Next, the HI of the bearing is constructed by computing the Bray–Curtis distance between the bearing’s degradation characteristics and health features. The cloud monitoring platform constantly tracks the health of the bearing and employs the MTBWO-BiGRU model to anticipate the forthcoming state of health. The platform generates an immediate alert when the HI of the bearing overtakes the alteration rate threshold and foresees the condition of the bearing. We compare the MTBWO-BiGRU model with the bidirectional long short-term memory (BiLSTM) and BiGRU models. The results indicate an accuracy level of 92.57%, which is evidently higher than that obtained when using the other two models. Moreover, the MTBWO-BiGRU model is lighter, demonstrating the practicality of the proposed approach.

show abstract

Bearing Fault Diagnosis Based on Multisensor Information Coupling and Attentional Feature Fusion

Cited by 13 publications

References 30 publications

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

Fruits hidden by green: an improved YOLOV8n for detection of young citrus in lush citrus trees

A novel multi-sensor hybrid fusion framework

An MTBWO Algorithm Based on BiGRU Model

Contact Info

Product

Resources

About