Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

doi:10.17559/tv-20221025165425

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…In order to further verify the superiority of the proposed method, this paper conducts comparative experiments using four advanced diagnostic methods: improved MobileNetV3 (Pro-MobileNetV3) [34], channel attention and multiscale convolutional neural network (CA-MCNN) [35], multiscale dynamic adaptive residual network (MSDARN) [36], and CBAM-ResNeXt50 [37]. All other parameters, except for the models themselves, are kept consistent, and the diagnostic results of different models on the SEU dataset are shown in table 3.…”

Section: Dataset Processingmentioning

confidence: 99%

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

Zhan,

Shao,

Men

et al. 2023

Meas. Sci. Technol.

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In a high-noise environment and with a limited number of faulty samples, it becomes challenging to extract a sufficient amount of useful fault information, which makes gear fault diagnosis more difficult. To address these issues, this paper proposes a fault diagnosis method for planetary gearboxes based on intrinsic feature extraction and attention mechanism. The method utilizes the complementary ensemble empirical mode decomposition algorithm to perform modal decomposition on the fault vibration signal, obtaining a series of modal components. By comparing and selecting the modal components that contain a significant amount of fault features, they are then transformed into two-dimensional images with time–frequency properties using wavelet transform. Additionally, a neural network model based on attention mechanism and large-scale convolution is proposed. The preprocessed images are inputted into the network for feature extraction. During this process, the large-scale convolution with residual structure maximizes the retention of effective feature information, while the attention network further filters the features. Finally, the selected features are used for fault classification. The model is validated using the gear datasets from Southeast University and the University of Connecticut. A comparison is made with the Pro-MobileNetV3, channel attention and multiscale convolutional neural network, multiscale dynamic adaptive residual network, and CBAM-ResNeXt50 models. It is found that the accuracy reaches 100% before adding Gaussian noise and 99.68% after adding noise, which is significantly higher than that of other models.

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Section: Dataset Processingmentioning

confidence: 99%

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

Zhan,

Shao,

Men

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Machine learning based mechanical fault diagnosis and detection methods: a systematic review

Xin,

Zhu,

Cai

et al. 2024

Meas. Sci. Technol.

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Mechanical fault diagnosis and detection are crucial for enhancing equipment reliability, economic efficiency, production safety, and energy conservation. In the era of Industry 4.0, artificial intelligence (AI) has emerged as a significant tool for mechanical fault diagnosis and detection, attracting considerable attention from both academia and industry. This review focuses on the application of AI techniques in mechanical fault diagnosis and detection using artificial intelligence techniques based on the existing research. It examines various AI algorithms including k-nearest neighbors, support vector machine, artificial neural network, deep learning, reinforcement learning, computer vision, and transformer algorithm integrating theoretical foundations with practical applications in industrial production. Furthermore, a comprehensive overview of these algorithms applications in mechanical fault diagnosis and detection is provided. Finally, a critical assessment highlights the advantages and limitations of these techniques, while forecasting the developmental trajectories of future intelligent diagnostic technologies based on machine learning. This review serves to bridge the gap between researchers in AI and fault diagnosis, contributing significantly to the field.

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Gearbox Fault Diagnosis Method Based on Improved MobileNetV3 and Transfer Learning

Cited by 2 publications

References 21 publications

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

Fault diagnosis method for planetary gearbox based on intrinsic feature extraction and attention mechanism

Machine learning based mechanical fault diagnosis and detection methods: a systematic review

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