Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets

Qin, Yufeng; Shi, Xiaodong

doi:10.3390/app12178474

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…x  is the i-th input feature map from the (l-1)-th layer. The input feature map is a specific input data pattern, and the l ij k represents the j-th kernel connected to the i-th input feature map, () act f represents the AF, l j b is the bias vector, and  represents the convolution operation [40].…”

Section: A Convolution Layer (Cl)mentioning

confidence: 99%

“…The AF is a crucial component in a CNN, and in this study, the Rectified Linear Unit (ReLU) is employed as the AF to alleviate issues of gradient explosion or vanishing [40]. The functional form of ReLU is denoted by: (2) c. Pooling Layer (PL) In CNN, a PL is employed to perform downsampling, which simplifies and refines the output feature map by reducing its dimensionality.…”

Section: B Activation Function (Af)mentioning

confidence: 99%

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A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

Liu,

Dong,

et al. 2024

IEEE Photonics J.

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In practical industrial environment, variable working condition can result in shifts in data distributions, and the labeled fault data in various working conditions is difficult to collect because rotating machines often works in normal status, and the insufficient labeled fault data brings data samples imbalance and performance degradation of intelligent fault diagnosis model. To overcome these problems, by integrating the superiority of deep learning method and feature-based transfer learning method, this work proposes an innovative cross-domain fault diagnosis framework based on deep transfer convolutional neural network and supervised joint matching. Firstly, the continue wavelet transform is used to process original bearing vibration signals and extract time-frequency images. Secondly, a deep transfer convolutional neural network is built by the way of fine-tuning, and the trained network is used to extract deep features from different domains. Thirdly, a new domain adaptation approach, supervised joint matching, is developed to conduct joint feature distribution matching and instance reweighting with the consideration of maximum marginal criterion. The intelligent bearing fault diagnosis model is then trained to predict the labels of the target domain's feature data. To verify the performance of the proposed approaches, this study uses two distinct datasets pertaining to bearing defects for conducting cross-domain fault diagnosis in the presence of balanced and imbalanced data. The experimental analysis indicates that the designed methods can achieve desirable diagnostic accuracy and possess robust generalization ability.

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Section: A Convolution Layer (Cl)mentioning

confidence: 99%

Section: B Activation Function (Af)mentioning

confidence: 99%

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

Liu,

Dong,

et al. 2024

IEEE Photonics J.

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“…To further validate the diagnostic effectiveness of the proposed method, it is compared and analyzed with some existing research results. (1) The S-CNN method first obtains the time-frequency diagram of the original data of the bearings through S-transform and then extracts the features through CNN for fault classification [36]. (2) The STFT-sparse autoencoder (SAE) method obtains the time-frequency diagram of the original vibration signal through STFT, uses a stacked SAE network to extract fault features, and realizes fault classification through softmax regression [37].…”

Section: Comparative Experimentsmentioning

confidence: 99%

Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features

Li,

Jia,

Liao

et al. 2024

Sensors

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In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.

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“…Generalized S-transform (GST) is a form of the time-frequency analysis method, which is a combination of time-domain signal analysis and frequency-domain signal analysis [23]. GST provides more detailed and comprehensive signal characterization in the time-frequency domain by jointly analyzing the signal in the time and frequency domains, which can obtain the instantaneous frequency information of the signal [24]. The principle of GST is based on the ideas of short-time Fourier transform (STFT) and continuous wavelet transform (CWT).…”

Section: Gstmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer

Xie,

Wang,

Zhu

et al. 2023

Electronics

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Aiming at rolling bearing fault diagnosis, the collected vibration signal contains complex noise interference, and one-dimensional information cannot be used to fully mine the data features of the problem. This paper proposes a rolling bearing fault diagnosis method based on SVD-GST combined with the Vision Transformer. Firstly, the one-dimensional vibration signal is preprocessed to reduce noise using singular value decomposition (SVD) to obtain a more accurate and useful signal. Then, the generalized S-transform (GST) is used to convert the processed one-dimensional vibration signal into a two-dimensional time–frequency image and make full use of the advantages of deep learning in image classification with higher recognition accuracy. In order to avoid the problem of limited sensory fields in CNN and the need for an RNN to compute step by step over time when processing sequence data, the use of a Vision Transformer model for pattern recognition classification is proposed. Finally, an experimental platform for the fault diagnosis of rolling bearings is built. The model is experimentally validated, achieving an average accuracy of 98.52% over multiple tests. Additionally, compared with the SVD-GST-2DCNN, STFT-CNN-LSTM, SVD-GST-LSTM, and GST-ViT fault diagnosis models, the proposed method has higher diagnostic accuracy and stability, providing a new method for rolling bearing fault diagnosis.

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Fault Diagnosis Method for Rolling Bearings Based on Two-Channel CNN under Unbalanced Datasets

Cited by 17 publications

References 42 publications

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

A New Bearing Fault Diagnosis Method Based on Deep Transfer Network and Supervised Joint Matching

Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features

Rolling Bearing Fault Diagnosis Based on SVD-GST Combined with Vision Transformer

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