Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review

Zhang, Xiao; Zhao, Boyang; Lin, Yun

doi:10.1109/access.2021.3128669

Cited by 86 publications

(33 citation statements)

References 53 publications

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“…It contains a large number of rolling bearing vibration signals in normal and fault conditions. In recent years, a large number of data-driven bearing fault diagnosis algorithms have used this data set for algorithm training and validation (Neupane et al , 2021; Zhang et al , 2021; Liu et al , 2022). Deep groove ball bearings use electron discharge machining single point damage to artificially form faults.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A bearing fault diagnosis method for high-noise and unbalanced dataset

Wang

Zhang²,

Liu³

et al. 2022

SRT

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Purpose The purpose is using generative adversarial network (GAN) to solve the problem of sample augmentation in the case of imbalanced bearing fault data sets and improving residual network is used to improve the diagnostic accuracy of the bearing fault intelligent diagnosis model in the environment of high signal noise. Design/methodology/approach A bearing vibration data generation model based on conditional GAN (CGAN) framework is proposed. The method generates data based on the adversarial mechanism of GANs and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed. Findings The method proposed in this paper is verified by the western reserve data set and the truck bearing test bench data set, proving that the CGAN-based data generation method can form a high-quality augmented data set, while the CGAN-based and improved residual with attention mechanism. The diagnostic model of the network has better diagnostic accuracy under low signal-to-noise ratio samples. Originality/value A bearing vibration data generation model based on CGAN framework is proposed. The method generates data based on the adversarial mechanism of GAN and uses a small number of real samples to generate data, thereby effectively expanding imbalanced data sets. Combined with the data augmentation method based on CGAN, a fault diagnosis model of rolling bearing under the condition of data imbalance based on CGAN and improved residual network with attention mechanism is proposed.

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Section: Experiments and Resultsmentioning

confidence: 99%

A bearing fault diagnosis method for high-noise and unbalanced dataset

Wang

Zhang²,

Liu³

et al. 2022

SRT

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“…This matrix can reflect the information related to the vibration signal, which will be called Gram matrix. 4)：Finally, by scaling each element in the Gram matrix to between 0 to 255 by (5), thereby converting the twodimensional matrix into a Gram angle field, where 𝐼(𝑗, 𝑘) represents the pixel value at point (𝑗, 𝑘) in the image. Here, 𝑖𝑛𝑡(•) is a rounding function, and 𝐺(𝑗, 𝑘) represents the element values corresponding to the 𝑗 row and the 𝑘 column in the Gram matrix.…”

Section: Gafmentioning

confidence: 99%

“…𝐼(𝑗, 𝑘) = 𝑖𝑛𝑡(127.5(𝐺(𝑗, 𝑘) + 1)) (5) When choosing between GADF and GASF, Tong and Zhang were all selected by subjectively contrasting the color difference, rather than strictly using an experiment, which lacks rigor. Therefore, to combine the respective characteristics of GADF and GASF, the point product of the Gram angle difference matrix and Gram angle sum matrix, namely, GADF•GASF, is produced according to (6).…”

Section: Gafmentioning

confidence: 99%

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Fault Diagnosis Method for Imbalanced Data of Rotating Machinery Based on Time Domain Signal Prediction and SC-ResNeSt

et al. 2023

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In an actual engineering environment, some rotating machines are usually in normal operation, but their time in a fault state is very short, which leads to a serious imbalance in the fault diagnosis datasets for rotating machinery, and gives the traditional network model the shortcomings of poor stability and low accuracy in practical engineering applications. To solve this problem, we propose a fault diagnosis method based on the combination of a new Dual-stage Attention-based Recurrent Neural Network (DA-RNN) and depth residual dispersion self-calibration convolution network (SC-ResNeSt). Firstly, a novel DA-RNN network with a gated cycle unit (GRU) as a coding-decoding unit was designed, and the network was used to predict and expand the scarce fault signals. Secondly, to make full use of the time domain information of vibration signals, a new image coding method, namely, Gram Angle Product Field (GAPF), was proposed. Then, because the traditional convolution layer lacks a dynamic receptive field to extract more representative features, self-calibrated convolution modules were introduced on the basis of the distraction network (ResNeSt), and a new network model, SC-ResNeSt, was established. Finally, the expanded vibration signal is converted into GAPF, which is used as the input for the SC-ResNeSt network to classify the fault types. To check the performance of the model, the Case Western Reserve University rolling bearing dataset and planetary gearbox dataset were used for testing. Ultimately, good results were obtained in a prediction experiment for bearing fault samples and a subsequent fault diagnosis experiment for bearings and gears, which verified the feasibility and practicability of the model.

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“…As the most common rotating machinery and equipment, rolling bearings work in complex and changeable harsh environments for a long time, so it is inevitable that there will be some damage, if not timely treatment. Because the failures will cause huge losses to industrial production, it is necessary to carry out scientific and efficient fault diagnosis research on rolling bearings [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning

Liu

Guan

et al. 2022

Sensors

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With the development of industrial manufacturing intelligence, the role of rotating machinery in industrial production and life is more and more important. Aiming at the problems of the complex and changeable working environment of rolling bearings and limited computing ability, fault feature information cannot be effectively extracted, and the current deep learning model is difficult to be compatible with lightweight and high efficiency. Therefore, this paper proposes a fault detection method for power equipment based on an energy spectrum diagram and deep learning. Firstly, a novel two-dimensional time-frequency feature representation method and energy spectrum feature map based on wavelet packet transform is proposed, and an energy spectrum feature map dataset is made for subsequent diagnosis. This method can realize multi-resolution analysis, fully extract the feature information contained in the fault signal, and accelerate the convergence of the subsequent diagnosis model. Secondly, a lightweight residual dense convolutional neural network model (LR-DenseNet) is proposed. This model combines the advantages of residual learning and a dense connection, and can not only extract deep features more easily, but can also effectively use shallow features. Then, based on the lightweight residual dense convolutional neural network model, an LR-DenseSENet model is proposed. By introducing the transfer learning strategy and adding the channel domain, an attention mechanism is added to the channel feature fusion layer, with the accuracy of detection up to 99.4%, and the amount of parameter calculation greatly reduced to one-fifth of that of VGG. Finally, through an experimental analysis, it is verified that the fault detection model designed in this paper based on the combination of an energy spectrum feature map and LR-DenseSENet achieves a satisfactory detection effect.

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Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review

Cited by 86 publications

References 53 publications

A bearing fault diagnosis method for high-noise and unbalanced dataset

A bearing fault diagnosis method for high-noise and unbalanced dataset

Fault Diagnosis Method for Imbalanced Data of Rotating Machinery Based on Time Domain Signal Prediction and SC-ResNeSt

Power Equipment Fault Diagnosis Method Based on Energy Spectrogram and Deep Learning

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