Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine

Wang, Rugen; Zhang, Shaohui; Chen, Zhuyun; Li, Weihua

doi:10.1016/j.measurement.2021.109467

Cited by 70 publications

(21 citation statements)

References 46 publications

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“…Liu et al [ 26 ] presented a rotating machinery fault diagnostics framework that is based on GANs and multisensor data fusion to generate synthetic data from the original data. Zhang et al [ 27 ], Wang et al [ 28 ], and Lv et al [ 29 ] all made use of one-dimensional time-domain signals to generate synthetic data using GANs for classification and diagnosis of rotating machinery. Similarly, Li et al [ 30 ], Wang et al [ 31 ], Zheng et al [ 32 ], and Wang et al [ 33 ] used one-dimensional frequency domain signals, and Huang et al [ 34 ] and Shi et al [ 35 ] used two-dimensional images while Pan et al [ 36 ], and Zhou et al [ 37 ] used one-dimensional feature sets to generate synthetic data.…”

Section: Literature Reviewmentioning

confidence: 99%

Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Ahang

Jalayer

Shojaeinasab

et al. 2022

Sensors

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Bearings are vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring are essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data are ample as systems usually work in desired conditions. On the other hand, fault data are rare, and in many conditions, there are no data recorded for the fault classes. Accessing fault data is crucial for developing data-driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on conditional generative adversarial networks (CGANs) was introduced. Trained on the normal and fault data on actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method was validated on a real-world bearing dataset, and fault data were generated for different conditions. Several state-of-the-art classifiers and visualization models were implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Ahang

Jalayer

Shojaeinasab

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Liu et al [26] presented a rotating machinery fault diagnostics framework that is based on GANs and multi-sensor data fusion to generate synthetic data from the original data. Zhang et al [27], Wang et al [28], and Lv et al [29] all made use of one-dimensional time-domain signals to generate synthetic data using GANs for classification and diagnosis of rotating machinery. Similarly, Li et al [30], Wang et al [31], Zheng et al [32], and Wang et al [33] used one-dimensional frequency domain signals, and Huang et al [34] and Shi et al [35] used two-dimensional images while Pan et al [36], and Zhou et al [37] used one-dimensional feature sets to generate synthetic data.…”

Section: Literature Reviewmentioning

confidence: 99%

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

Ahang¹,

Jalayer²,

Shojaeinasab³

et al. 2022

Preprint

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Bearings are one of the vital components of the rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data is ample as systems usually work in desired conditions. On the other hand, fault data is rare, and in many conditions, there is no data recorded for the fault classes. Accessing fault data is crucial for developing data driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on Conditional Generative Adversarial Networks (CGANs) is introduced. Trained on the normal and fault data on any actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions. Several state-of-the-art classifiers and visualization models are implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.

show abstract

“…Wang et al [24] used the analog signals generated by WGAN to expand the unbalanced dataset and train stacked autoencoders to detect the health status of mechanical equipment. Wang et al [25] used enhanced deep convolutional GAN to generate more fault samples and figure out the problem of imbalanced data, thus improving fault classification accuracy. Peng et al [26] proposed the reinforcement auxiliary classification GAN, which stabilized the training process by using boundary-seeking loss and introduced cost-sensitive learning to alleviate data imbalance in the Tennessee Eastman dataset.…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples

et al. 2023

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Enhanced generative adversarial network for extremely imbalanced fault diagnosis of rotating machine

Cited by 70 publications

References 46 publications

Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

Data Augmentation and Intelligent Fault Diagnosis of Planetary Gearbox Using ILoFGAN Under Extremely Limited Samples

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