Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Pu, Haoning; Wen, Zhan; Sun, Xiulan; Han, Lemei; Na, Yanhe; Liu, Hantao; Li, Wenzao

doi:10.1108/ijicc-09-2022-0253

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Finally, the extracted singular value feature matrix was input into CNN for fault diagnosis. Pu et al [36] proposed a data preprocessing method that combines incomplete empirical mode decomposition (IEMD) with Mel frequency cepstrum coefficient. Firstly, the sound signal was decomposed using IEMD to obtain a large number of IMF and residuals (RES).…”

Section: Modal Decomposition Algorithmmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

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The health condition of rolling bearings has a direct impact on the safe operation of high-speed trains. And their working environment is harsh and the working condition is complex, which brings challenges to fault diagnosis. With the development of computer technology, deep learning has been applied in the field of fault diagnosis and has rapidly developed. Among them, convolutional neural network (CNN) has received great attention from researchers due to its powerful data mining ability and feature adaptive learning ability. Based on recent research hotspots, the development history and trend of CNN is summarized and analyzed. Firstly, the basic structure of CNN is introduced and the important progress of classical CNN models for rolling bearing fault diagnosis in recent years is studied. The problems with the classic CNN algorithm have been pointed out. Secondly, to solve the above problems, combined with recent research achievements, various methods and principles for optimizing CNN are introduced and compared from the perspectives of deep feature extraction, hyperparameter optimization, network structure optimization. Although significant progress has been made in the research of fault diagnosis of rolling bearings based on CNN, there is still room for improvement and development in addressing issues such as low accuracy of imbalanced data, weak model generalization, and poor network interpretability. Therefore, the future development trend of CNN networks is discussed finally. And transfer learning models are introduced to improve the generalization ability of CNN and interpretable CNN is used to increase the interpretability of CNN networks.

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Section: Modal Decomposition Algorithmmentioning

confidence: 99%

A review on convolutional neural network in rolling bearing fault diagnosis

Li,

Ma,

Yuan

et al. 2024

Meas. Sci. Technol.

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Weighted multi-source information fusion network-based framework for mechanical fault diagnosis

Song,

Zhang,

2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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To alleviate the loss of potentially valuable and discriminative features as the number of networks increases in deep learning (DL). A weighted multi-source information fusion network-based framework is proposed for mechanical fault diagnosis. Firstly, a feature improved Deep Belief Network (FIDBNs) and a feature improved Convolutional Neural Network (FICNNNs) are designed to repetitively extract valuable information from original data. Meanwhile, an attention mechanism is adopted to further extract important fault characteristics Then, a weighted feature fusion module is used to integrate better for more diagnostic performance. Finally, an experimental dataset based on bearing failures is used to prove the effectiveness and superiority of the proposed framework compared with other methods.

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Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

Cited by 2 publications

References 37 publications

A review on convolutional neural network in rolling bearing fault diagnosis

A review on convolutional neural network in rolling bearing fault diagnosis

Weighted multi-source information fusion network-based framework for mechanical fault diagnosis

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