A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Gao, Yangde; Kim, Cheol Hong; Kim, Jong‐Myon

doi:10.3390/s21196614

Cited by 20 publications

(10 citation statements)

References 24 publications

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“…In order to further evaluate the performance of the method, we compare it with the state-of-art deep learning-based fault diagnosis methods. These methods include CNN-gcForest hybrid model(CNN-gcForest) [32], EWDCNN-LSTM hy- brid method(NHDLM) [33], improved residual dense networks(IRDN) [34]. Table10 shows the statistical results.…”

Section: ) Performance Analysis and Comparisonsmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Zhao

Zhang

et al. 2023

IEEE Access

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The main bearing is the core component of gas-fired generator, and its reliability directly affects the stability of the whole system. Therefore, it is of great significance to study the fault diagnosis of the main bearing of gas-fired generator. In the bearing fault diagnosis based on vibration signal, how to extract the signature features of fault effectively is the key to achieving accurate fault diagnosis. Based on extracting the signature features of faults, how to classify the fault features efficiently is another key to achieving accurate fault diagnosis. Based on this, we propose a bearing fault diagnosis method based on Mel frequency cepstrum coefficient (MFCC) and deformable space-frequency attention network (DSFAN). In view of the inconsistent feature distribution of different types of faults, the MFCC algorithm is introduced to preprocess the original fault signals and extract their signature features. Then, the network model DSFAN is constructed based on the space-frequency feature attention mechanism (SFFAM). DSFAN can extract the global constraint features and distributed constraint features of fault signals and realize bearing fault diagnosis. To make full use of classification information, the data processed by MFCC is constructed into a three-dimensional data cube as the input of DSFAN. Finally, the validity of the proposed method MFCC-DSFAN is verified on CWRU, XJTU, and gas-fired generator data sets. The experimental results show the excellent performance of MFCC-DSFAN for fault diagnosis and prove the effectiveness of the attention module in feature extraction.

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Section: ) Performance Analysis and Comparisonsmentioning

confidence: 99%

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Zhao

Zhang

et al. 2023

IEEE Access

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“…A wide core is used in the first convolution layer to reduce high-frequency noise better. 7 The multi-layer small convolution kernel enhances network performance and makes the network deeper. To speed up the training process, batch normalization is performed after the convolutional layer and the fully connected layer.…”

Section: Network Modelmentioning

confidence: 99%

A FPGA accelerator-based WDCNN for rolling bearing diagnosis

Yang¹,

Xu²,

Li³

2022

International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022)

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In industrial practice, deploying a neural network for the real-time diagnosis of rolling bearings is a challenging task. To address this problem, this paper proposes a method for deploying Deep Convolutional Neural Networks with Wide Firstlayer Kernels (WDCNN) on a field programmable gate array (FPGA). Vibration signals can be used to predict the bearing condition directly by the accelerator. Using high-level synthesis tools to optimize the accelerator, the prediction time of a sample (2,048 points) has been reduced to milliseconds. Our research results indicate that the accelerator can be used to accurately and rapidly predict the bearing state.

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“…Xiang et al [20] introduced attention mechanisms into CNN, augmenting the model's fault feature extraction capabilities by emphasizing critical information. Gao et al [21] merged enhanced CNN and LSTM, yielding promising results in the domain of diagnosing faults in rotating machinery. Shen et al [22] seamlessly integrated domain-specific knowledge with deep CNN, deploying this approach for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis framework of rolling bearings based on adaptive dynamic activation convolutional capsule network

Jiang,

Li,

et al. 2024

Meas. Sci. Technol.

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This paper presents a fault diagnosis framework of rolling bearings based on the Adaptive Dynamic Activation Convolutional Capsule Network (ADAC-CN). The Capsule network is first used to vectorize and mine the spatial information of features Aiming at extracting more comprehensive spatial location features. Then, the feature extraction layer of the capsule network is improved to extract deeper features and reduce the number of parameters. The dynamic activation function is then introduced to extract features better than the steady-state activation function, which can self-adapt the activation features and capture variable feature information. Finally, real rolling bearing data sets are used to verify the superiority and effectiveness of the proposed method with the assistance of comparisons with existing fault diagnosis methods. The results confirmed that the proposed framework has better performance in terms of accuracy and generalization.

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A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Cited by 20 publications

References 24 publications

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

Bearing Fault Diagnosis Based on Mel Frequency Cepstrum Coefficient and Deformable Space-Frequency Attention Network

A FPGA accelerator-based WDCNN for rolling bearing diagnosis

A novel fault diagnosis framework of rolling bearings based on adaptive dynamic activation convolutional capsule network

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