Imbalanced Sample Fault Diagnosis of Rolling Bearing Using Deep Condition Multidomain Generative Adversarial Network

Liu, Xuejun; Sun, Wei; Li, Hongkun; Wang, Zibo; Li, Qiang

doi:10.1109/jsen.2022.3222535

Cited by 9 publications

(5 citation statements)

References 43 publications

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“…To further demonstrate the superiority of the proposed method, we compare it with recently published articles in the field. These methods are dual discriminator GAN (D2GAN) [32], condition multidomain GAN (CMDGAN) [33], and two-stage GAN (2S-GAN) [34].…”

Section: Methods For Comparisonmentioning

confidence: 99%

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

Wang,

Zhang,

Han

et al. 2024

Meas. Sci. Technol.

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As a vital constituent of rotating machinery, rolling bearings assume a pivotal function in ensuring the stable operation of equipment. Recently, deep learning (DL)-based methods have been able to diagnose bearing faults accurately. However, in practical applications, the severe data imbalance caused by the limited availability of fault data compared to the abundance of healthy data poses challenges to the effective training of DL models, leading to a decrease in diagnostic accuracy. In this paper, a bearing fault diagnosis method with the improved residual Unet diffusion model (IResUnet-DM) based on a data generation strategy is proposed to solve the extreme data imbalance. Initially, a deep feature extraction network named improved residual Unet is built, which effectively enhances the information learning ability from vibration signals of the Unet network by one-dimensional residual block and self-attention block. Furthermore, the IResUnet-DM is constructed, which generates vibration signals under extreme data imbalance based on a probability model. The variational bound on the negative log-likelihood of the distribution of generated data was optimized to make the generated data similar to the real data distribution. Finally, wide deep convolutional neural network and one-dimensional ResNet classification networks were used for fault identification to verify the validity and generalization of the IResUnet-DM. Experiment results at different data imbalance rates on two bearing datasets demonstrate that the proposed method can effectively improve fault diagnosis accuracy under extreme data imbalances and outperform the comparison method.

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Section: Methods For Comparisonmentioning

confidence: 99%

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

Wang,

Zhang,

Han

et al. 2024

Meas. Sci. Technol.

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show abstract

“…This phase starts with statistical analysis and AI application i.e. application of algorithms for classification, clustering, or regression Feature Extraction [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73] 2023 Data Augmentation [74], [75], [76], [38], [77], [78], [79], [80], [81], [82], [83] 2023 Imbalanced Learning [84], [85], [86], [87], [88] 2023 Noise Removal [89], [90], [64], [91], [92], [22] 2023 Multi-scale Time series Analysis [93], [43], [94], [95], [96],…”

Section: Classification Of Intelligent Fault Diagnosis (Ifd) Methodsmentioning

confidence: 99%

“…They compare their method with five existing methods and show that their method achieves the highest accuracy, stability, and efficiency in all transfer tasks. Xuejun Liu et al has proposed a condition multidomain generative adversarial network that generates synthetic fault samples from limited real data and improves the accuracy of fault diagnosis [78]. This method introduces a self-adaptive feature extraction module to construct the sample condition information and a self-attention mechanism to capture the local and global fault features.…”

Section: Data Augmentationmentioning

confidence: 99%

“…Adversarial training and transfer learning. Xuejun Liu et.al have proposed a generative adversarial network that can handle imbalanced fault samples, the framework captures distribution information by fusing two pieces of information from two domains [24]. Jiuyuan Huo et.al has used a self-attention mechanism with residual networks to extract features GAN fitting the data distribution to generate highquality samples of balanced dataset [25].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Khan,

Akram,

Khattak

et al. 2024

Preprint

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Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes.Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap,we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration.

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“…Meng et al [14] based on the original ACGAN-GP, incorporated the CBAM attention mechanism into the diagnostic network, improving the representation ability of the fault diagnosis network and the generalization performance to achieve highprecision fault diagnosis. Liu et al [15] proposed a conditional multi-domain GAN that fuses two-domain information and employs a self-attention mechanism to capture detailed fault features, which showed improved performance in experiments. Miao et al [16] proposed an improved VAEGAN that merges the benefits of VAE and GAN, capturing data distribution and generating high-quality samples to balance the dataset.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis in electric motors based on IDIG-GAN under small sample conditions

Song,

Liu,

Wang

2024

Meas. Sci. Technol.

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The bearing fault diagnosis based on deep learning algorithms requires a substantial amount of data. However, in practical industrial production, the diagnostic algorithms tend to work ineffectively due to the limitations of samples. Therefore, in this article, we propose an improved method of deep convolutional generative adversarial networks with discriminator gradient gap regularization (IDIG-GAN), which can effectively solve the problems of unstable training and poor training performance under a small sample dataset. Firstly, the self-attention mechanism is integrated into the DCGAN to capture global information to enhance the generalization capability of the network. Moreover, gradient normalization is applied to the discriminator to address the problem of vanishing gradients in the network. Furthermore, gradient gap regularization is incorporated into the loss function to narrow the gap between the discriminator gradient norms, thereby improving network stability when dealing with small fault datasets. Through training with the improved IDIG-GAN, then the generated samples are used to expand the dataset and construct a fault diagnosis model. By verifying under two bearing datasets, the results demonstrate that the proposed method can generate high-quality samples and effectively enhance the diagnostic capability of the network when working with small datasets.

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Imbalanced Sample Fault Diagnosis of Rolling Bearing Using Deep Condition Multidomain Generative Adversarial Network

Cited by 9 publications

References 43 publications

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments

Rolling bearing fault diagnosis in electric motors based on IDIG-GAN under small sample conditions

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Imbalanced Sample Fault Diagnosis of Rolling Bearing Using Deep Condition Multidomain Generative Adversarial Network

Cited by 9 publications

References 43 publications

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

A bearing fault diagnosis method with an improved residual Unet diffusion model under extreme data imbalance

Deep Learning based Approaches for Intelligent Industrial Machinery Health Management &amp; Fault Diagnosis in Resource-Constrained Environments

Rolling bearing fault diagnosis in electric motors based on IDIG-GAN under small sample conditions

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Deep Learning based Approaches for Intelligent Industrial Machinery Health Management & Fault Diagnosis in Resource-Constrained Environments