Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group

Han, Changkun; Lü, Wei; Wang, Huaqing; Song, Liuyang; Cui, Lingli

doi:10.1016/j.ymssp.2022.109995

Cited by 32 publications

(6 citation statements)

References 40 publications

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“…Two 1DRes blocks extract the concatenated features, which are subsequently upsampled by the Upsample block. The Upsample block consists of linear interpolation and Conv1d (1,3), where Conv1d brings learnability to upsampling. Finally, the features are mapped by GN, SiLU, and Conv1d (1,3) to ϵ θ (s t , t), which is the Gaussian noise added at diffusion step t.…”

Section: The Iresunet Networkmentioning

confidence: 99%

“…carry the system of variable speed and power transmission, assuming a pivotal function in ensuring the stable operation of equipment [2]. However, severe operating conditions and equipment malfunctions frequently result in unavoidable bearing wear and tear, fatigue cracks, and other problems, impacting the safety of the equipment and potentially resulting in catastrophic safety accidents [3]. Hence, achieving intelligent fault diagnosis for bearings holds immense importance.…”

Section: Introductionmentioning

confidence: 99%

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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: The Iresunet Networkmentioning

confidence: 99%

Section: Introductionmentioning

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.

Self Cite

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show abstract

“…In this study, different loads at the same speed were used for validation experiments. The speed of the motor was set at 1800 rpm, and the load of the eddy current brake was set at 30 Nm, 35 To fully validate the effectiveness of the proposed method, eight transfer learning schemes are designed. The specific working conditions of eight kinds of transfer learning schemes are shown in table 3.…”

Section: Validation Experiments Amentioning

confidence: 99%

Domain adaptive networks with limited data for rotating machinery fault diagnosis: a case of study of gears

Li,

Yu,

et al. 2023

Meas. Sci. Technol.

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Rotating machinery is one of the most common components in the industry. Therefore, timely and accurate fault diagnosis of rotating machinery is essential for the regular operation of equipment. At present, some achievements have been made in rotating machinery fault diagnosis based on a large number of marked fault data. However, most of the machines are in a normal state in actuality. Especially, the machines run under different loads, so it is costly to collect a large number of labeled fault data under different load distributions. To solve rotating machinery fault diagnosis in different load conditions with limited samples, a domain adaptive group convolutional neural network is proposed. Firstly, the least squares generative adversarial networks were used to expand the limited target sample data. By changing the objective function, the two defects of the low quality of the vibration signal generated by the traditional generative adversarial networks and the unstable training process are optimized. Secondly, the raw vibration signals in the source domain are pre-trained by the group convolutional neural network, and the group training network effectively reduces network parameters. Finally, the source domain signals and target domain signals were trained in domain adversarial networks to diagnose different distributed data in target domains. The proposed method is validated by collecting the raw vibration signals of gears under different loads and different health states, and the effectiveness of the proposed method is proved. Experimental validation shows that the method proposed in this paper achieves an average accuracy improvement of more than 12% compared to other existing methods.

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“…In the modern industry, it is critical to keep the equipment running safely and stably [ 1 ]. As the integral parts of equipment, rotating components such as bearings and gears are prone to fault due to the harsh working environment, which will affect the stable operation of the equipment.…”

Section: Introductionmentioning

confidence: 99%

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

Wang

Tong

et al. 2023

Sensors

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The application of transfer learning in fault diagnosis has been developed in recent years. It can use existing data to solve the problem of fault recognition under different working conditions. Due to the complexity of the equipment and the openness of the working environment in industrial production, the status of the equipment is changeable, and the collected signals can have new fault classes. Therefore, the open set recognition ability of the transfer learning method is an urgent research direction. The existing transfer learning model can have a severe negative transfer problem when solving the open set problem, resulting in the aliasing of samples in the feature space and the inability to separate the unknown classes. To solve this problem, we propose a Weighted Domain Adaptation with Double Classifiers (WDADC) method. Specifically, WDADC designs the weighting module based on Jensen–Shannon divergence, which can evaluate the similarity between each sample in the target domain and each class in the source domain. Based on this similarity, a weighted loss is constructed to promote the positive transfer between shared classes in the two domains to realize the recognition of shared classes and the separation of unknown classes. In addition, the structure of double classifiers in WDADC can mitigate the overfitting of the model by maximizing the discrepancy, which helps extract the domain-invariant and class-separable features of the samples when the discrepancy between the two domains is large. The model’s performance is verified in several fault datasets of rotating machinery. The results show that the method is effective in open set fault diagnosis and superior to the common domain adaptation methods.

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Multistate fault diagnosis strategy for bearings based on an improved convolutional sparse coding with priori periodic filter group

Cited by 32 publications

References 40 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

Domain adaptive networks with limited data for rotating machinery fault diagnosis: a case of study of gears

Cross-Domain Open Set Fault Diagnosis Based on Weighted Domain Adaptation with Double Classifiers

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