Weak fault detection with a two-stage key frequency focusing model

Gao, Dawei; Zhu, Yan; Kang, Wei; Fu, Hong; Yan, Ke; Ren, Zhijun

doi:10.1016/j.isatra.2021.06.014

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…However, under complex working conditions such as high speed, frequent load changes, rolling bearings are prone to occur aging and wear, which will lead to equipment damage, huge economic losses and even safety accidents. Therefore, to ensure normal and stable operation of mechanical equipment, fault diagnosis topic of rolling bearings has attracted more and more attention from engineers and experts [1,2].…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method based on dilated convolution and attention for rolling bearing under multiple working conditions and noisy environments

Zhang,

Liu,

et al. 2023

J. vibroeng.

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As essential equipment in rotating machinery, the fault diagnosis technology of rolling bearings has achieved great success. However, it still suffers from limitations in terms of generalization and noise resistance performance when operating under complex conditions. To accurately identify the fault types of rolling bearings under different loads and nosy environments, a novel intelligent fault diagnosis method is proposed. Firstly, the utilization of dilated convolution expands the network's receptive field, thereby effectively enhancing the scope of fault extraction. Then, by incorporating the Efficient Channel Attention (ECA) in different convolutional layers, the extracted features are adaptively recognized, highlighting important representation information and improving fault diagnosis performance. Finally, the proposed network is utilized for rolling bearing fault diagnosis under diverse operating and noise conditions, and its efficacy is evaluated on various datasets. The experimental results demonstrate that the proposed method exhibits good generalization performance and strong robustness, compared with other methods.

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Section: Introductionmentioning

confidence: 99%

A fault diagnosis method based on dilated convolution and attention for rolling bearing under multiple working conditions and noisy environments

Zhang,

Liu,

et al. 2023

J. vibroeng.

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“…5 Specifically, popularization of AIS data provides with enough data support for ship trajectory prediction and risk avoidance, 6 and the development of machine/deep learning algorithms also present a powerful algorithm support. 7 In fact, trajectory prediction problem has been widely investigated in various fields, including vehicle trajectory prediction, 8,9 pedestrian trajectory prediction, 10,11 time-series sequence prediction, 12,13 among others. The moving objects in different circumstances have their own characteristics, as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, trajectory prediction problem has been widely investigated in various fields, including vehicle trajectory prediction, 8,9 pedestrian trajectory prediction, 10,11 time-series sequence prediction, 12,13 among others. The moving objects in different circumstances have their own characteristics, as shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

A novel long sequence multi-step ship trajectory prediction method considering historical data

Gao

Wang

Zhu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part M:

Self Cite

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Considering the importance of timeliness in ship risk assessment, a high-resolution and long sequence multi-step trajectory prediction method is proposed. Through the multi-step prediction and uncertainty analysis of the ship trajectory for a long period of time, it is possible to make timely ship navigation risk assessment. First, the ship trajectory with high resolution is obtained by cubic spline interpolation. Then, Dynamic Time Warping (DTW) is used as the metric of distance, and a method called Laplacian Eigenmaps Self-Organizing Map (LE-SOM) is used to extract the features from original high-dimensional data with unequal intervals, so as to select the historical trajectories that can be used as a reference. Finally, the trajectory is generated for multi-step prediction. The proposed method not only predicts the position of the ship and its uncertainty from statistical perspective, but also investigates the relationship between trajectory curvature and the prediction error. The case study on a ferry ship in the Jiangsu section of the Yangtze River indicates the validity of the method.

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Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

Wei

Jin

et al. 2023

Appl Intell

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Weak fault detection with a two-stage key frequency focusing model

Cited by 5 publications

References 39 publications

A fault diagnosis method based on dilated convolution and attention for rolling bearing under multiple working conditions and noisy environments

A fault diagnosis method based on dilated convolution and attention for rolling bearing under multiple working conditions and noisy environments

A novel long sequence multi-step ship trajectory prediction method considering historical data

Intelligent fault diagnosis and health stage division of bearing based on tensor clustering and feature space denoising

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