An Efficient Incremental Learning of Bearing Fault Imbalanced Data Set via Filter StyleGAN

Wang, Yinjun; Zeng, Liling; Wang, Liming; Shao, Yimin; Zhang, Yongxiang; Ding, Xiaoxi

doi:10.1109/tim.2021.3073436

Cited by 24 publications

(4 citation statements)

References 27 publications

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“…As we all know, t-Distributed Stochastic Neighbor Embedding (t-SNE) [24] is a prominent dimensionality reduction tool that maps high-dimensional data into 2D or 3D, offering a clearer visualization of data similarities. In our research, t-SNE is utilized to highlight the similarity and diversity between real and synthetically generated samples.…”

Section: Experiments and Discussionmentioning

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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Section: Experiments and Discussionmentioning

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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“…Rolling bearings play a key role in mechanical equipment. Any tiny fault can affect the operation of the equipment or even produce latent threats to lives and property [ 1 – 4 ]. Therefore, it is necessary to monitor the health status of mechanical equipment.…”

Section: Introductionmentioning

confidence: 99%

Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network

Liu

Yang

et al. 2022

Computational Intelligence and Neuroscience

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The health status of mechanical bearings concerns the safety of equipment usage. Therefore, it is of crucial importance to monitor mechanical bearings. Currently, deep learning is the mainstream approach for this task. However, in practical situations, the majority of fault samples have the issue of severe class unbalancing, which renders conventional deep learning inapplicable. Targeted at this issue, this paper proposes an invariant temporal-spatial attention fusion network called ITSA-FN for bearing fault diagnosis under unbalanced conditions. First, the proposed method utilizes the invariant temporal-spatial attention representation section, which consists of a pretrained convolutional auto-encoder model, a convolutional block attention module, and a long short-term memory network, to extract independent features and invariant features of spatial-temporal characteristics from input signals. Then, a multilayer perceptron is used to fuse and infer from the extracted features and design a new loss function from the focal loss for network training. Finally, this article validates proposed model’s effectiveness through comparative experiments, ablation studies, and generalization performance experiments.

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“…Incremental learning can handle this problem. Regarding bearing fault diagnosis, incremental learning methods including class incremental learning [20][21][22][23] and imbalanced data learning [24,25] have been developed. However, few studies have focused on catastrophic forgetting.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method

et al. 2022

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In recent years, deep-learning schemes have been widely and successfully used to diagnose bearing faults. However, as operating conditions change, the distribution of new data may differ from that of previously learned data. Training using only old data cannot guarantee good performance when handling new data, and vice versa. Here, we present an incremental learning scheme based on the Repeated Replay using Memory Indexing (R-REMIND) method for bearing fault diagnosis. R-REMIND can learn new information under various working conditions while retaining older information. First, we use a feature extraction network similar to the Inception-v4 neural network to collect bearing vibration data. Second, we encode the features by product quantization and store the features in indices. Finally, the parameters of the feature extraction and classification networks are updated using real and reconstructed features, and the model did not forget old information. The experiment results show that the R-REMIND model exhibits continuous learning ability with no catastrophic forgetting during sequential tasks.

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An Efficient Incremental Learning of Bearing Fault Imbalanced Data Set via Filter StyleGAN

Cited by 24 publications

References 27 publications

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

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

Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network

Bearing Fault Diagnosis via Incremental Learning Based on the Repeated Replay Using Memory Indexing (R-REMIND) Method

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