Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives

Chen, Jinglong; Zhang, Tianci; He, Shuilong; Lv, Haixin

doi:10.1016/j.isatra.2021.11.040

Cited by 101 publications

(28 citation statements)

References 49 publications

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“…For example, the trained generator can be used to fix a faulty sample, and the fault can then be located by sample comparison [ 51 ]. Moreover, this adversarial learning strategy of GAN has also been widely implemented to tackle the problem of domain shift of data distribution for fault diagnosis under different working conditions or environments, i.e., the distribution of available training data in the source domain is different from that of data to be tested in the target domain, making the trained model hard to be generalized [ 52 ]. It is a very challenging issue usually faced by industrial applications.…”

Section: Part I: Unsupervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

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Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the captured sensory data, and also predict their failures in advance, which can greatly help to take appropriate actions for maintenance and avoid serious consequences in industrial systems. In recent years, deep learning methods are being widely introduced into FDP due to the powerful feature representation ability, and its rapid development is bringing new opportunities to the promotion of FDP. In order to facilitate the related research, we give a summary of recent advances in deep learning techniques for industrial FDP in this paper. Related concepts and formulations of FDP are firstly given. Seven commonly used deep learning architectures, especially the emerging generative adversarial network, transformer, and graph neural network, are reviewed. Finally, we give insights into the challenges in current applications of deep learning-based methods from four different aspects of imbalanced data, compound fault types, multimodal data fusion, and edge device implementation, and provide possible solutions, respectively. This paper tries to give a comprehensive guideline for further research into the problem of intelligent industrial FDP for the community.

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Section: Part I: Unsupervised DL Methods For Intelligent Industrial Fdpmentioning

confidence: 99%

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Qiu

Cui

Ping

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…This capability exhibited by the generative adversarial network has made its application in intelligent fault diagnosis. Pan et al [ 25 ] reviewed the related literature on small sample-focused fault diagnosis methods using GANs. Their paper describes the GAN approaches and reviews GAN-based intelligent fault diagnosis applications in the literature while discussing the limitations and future road maps of GAN-based fault diagnosis applications.…”

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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“…This capability exhibited by the Generative Adversarial Network has made its application in intelligent fault diagnosis vast. Pan et al [25] reviewed the related literature on small-sample-focused fault diagnosis methods using GANs. Their paper describes the GAN approaches and reviews GAN-based Intelligent Fault Diagnosis applications in literature while discussing the limitations and future road maps of GAN-based fault diagnosis applications.…”

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

Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives

Cited by 101 publications

References 49 publications

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review

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

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