Deep Transfer Learning Method Based on 1D‐CNN for Bearing Fault Diagnosis

He, Jun; Li, Xiang; Chen, Yong; Chen, Danfeng; Guo, Jing; Zhou, Yan

doi:10.1155/2021/6687331

Cited by 35 publications

(18 citation statements)

References 32 publications

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“…In 2021, Ozcan et al went on to improve the 1D CNN for the purpose of fault diagnosis by enhancing the input fault features with multi-channel and multi-level inputs [22]. Furthermore, using 1D CNN, He et al used Correlation Alignment (CORAL) to minimize the difference in the marginal distribution between the source and target domains [23]. However, it is much easier to extract information from data in a high dimension [24].…”

Section: Introductionmentioning

confidence: 99%

A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Han

Zhang

Sun

et al. 2021

Sensors

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Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.

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Section: Introductionmentioning

confidence: 99%

A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Han

Zhang

Sun

et al. 2021

Sensors

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show abstract

“…The amount of neurons in the second full connection layer is consistent with the amount of malfunction categories. The target output category is realized by softmax regression classifier [25].…”

Section: D-cnn Methodsmentioning

confidence: 99%

Study on blade fault diagnosis of free flying UAV based on multiparameter and deep learning method

Li,

Zhang,

et al. 2023

Eng. Res. Express

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With the widespread application of unmanned aerial vehicles (UAVs) in various fields, more and more attention has been paid to the operation status monitoring and fault diagnosis of UAVs. During the use of drones, the motors, blades, connectors and other components may inevitably experience wear, fatigue, and breakage, which are difficult to directly monitor through sensors. Therefore, a fault identification method based on one-dimensional convolutional neural network (1D-CNN) is proposed to provide ideas for the research on the mechanical fault diagnosis of UAVs. A Bluetooth wireless acceleration-attitude sensor is used to collect the acceleration, angular velocity and angle of the free flying UAV in X, Y, and Z directions. With these characteristic parameters as sample data, fault identification can be implemented using deep learning model. Besides, to deal with over-fitting, a data reconstruction method of partition sampling is proposed. By comparing different input parameters and optimization functions, we found that when using multi-parameter + RMSProp optimization, the recognition accuracy reaches 98%. In addition, a comparative analysis is carried out using shallow neural network PNN and SVM methods, and the results show that the proposed 1D-CNN model outperforms both shallow neural networks and traditional machine learning methods.

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“…D Neupane presented the method that detects bearing failures using the continuous wavelet transform and classifies them using a switchable normalization-based convolutional neural network [ 24 ]. To solve the problem of a lack of labeled samples with the same distribution in real industry, J He proposed a deep transfer learning method based on special 1D-CNN for rolling bearing fault diagnosis [ 25 ]. Zheng designed a new fault diagnosis method using deformable CNN, deep long short-term memory and transfer learning strategies.…”

Section: Methodsmentioning

confidence: 99%

Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection

Liu

Wang

Li³

2022

Sensors

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Pipeline operational safety is the foundation of the pipeline industry. Inspection and evaluation of defects is an important means of ensuring the safe operation of pipelines. In-line inspection of Magnetic Flux Leakage (MFL) can be used to identify and analyze potential defects. For pipeline MFL identification with inspecting in long distance, there exists the issues of low identification efficiency, misjudgment and leakage judgment. To solve these problems, a pipeline MFL inspection signal identification method based on improved deep residual convolutional neural network and attention module is proposed. A improved deep residual network based on the VGG16 convolution neural network is constructed to automatically learn the features from the MFL image signals and perform the identification of pipeline features and defects. The attention modules are introduced to reduce the influence of noises and compound features on the identification results in the process of in-line inspection. The actual pipeline in-line inspection experimental results show that the proposed method can accurately classify the MFL in-line inspection image signals and effectively reduce the influence of noises on the feature identification results with an average classification accuracy of 97.7%. This method can effectively improve identification accuracy and efficiency of the pipeline MFL in-line inspection.

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Deep Transfer Learning Method Based on 1D‐CNN for Bearing Fault Diagnosis

Cited by 35 publications

References 32 publications

A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field

Study on blade fault diagnosis of free flying UAV based on multiparameter and deep learning method

Attention Module Magnetic Flux Leakage Linked Deep Residual Network for Pipeline In-Line Inspection

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