Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis

Ghorvei, Mohammadreza; Kavianpour, Mohammadreza; Beheshti, Mohammad Taghi Hamidi; Ramezani, Amin

doi:10.1016/j.neucom.2022.10.057

Cited by 61 publications

(15 citation statements)

References 32 publications

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“…The first category performs data augmentation to deal with the requirements of DL models for fault diagnosis, particularly to mitigate issues related to imbalanced datasets [130]. The second category harnesses adversarial principles to facilitate transfer learning in fault diagnosis [131][132][133]. For the first category, several GAN variants such Wasserstein GAN (WGAN), Wasserstein GAN with gradient penalty (WGAN-GP), and conditional GAN (CGAN) are applied in rotating machinery fault diagnosis.…”

Section: Ganmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

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Deep learning has attained remarkable achievements in diagnosing faults for rotary machineries. Capitalizing on the formidable learning capacity of deep learning, it has the potential to automate human labor and augment the efficiency of fault diagnosis in rotary machinery. These advantages have engendered escalating interest over the past decade. Although recent reviews of the literature have encapsulated the utilization of deep learning in diagnosing faults in rotating machinery, they no longer encompass the introduction of novel methodologies and emerging directions as deep learning methodologies continually evolve. Moreover, in practical application, novel issues and trajectories perpetually manifest, demanding a comprehensive exegesis. To rectify this lacuna, this article amalgamates current research trends and avant-garde methodologies while systematizing the utilization of anterior deep learning techniques. The evolution and extant status of deep learning in diagnosing faults for rotary machinery were delineated, with the intent of providing orientation for prospective research. Over the bygone decade, archetypal deep learning theory has empowered the diagnosis of faults in rotating machinery by directly establishing the nexus between mechanical data and fault conditions. In recent years, meta learning methods aimed at solving small sample scenarios and large model transformers aimed at mining big data features have both received widespread attention and development in the field of fault diagnosis of rotating machinery equipment. Although excellent results have been achieved in these two directions, there is no review and summary article yet, so it is necessary to update the review literature in the field of fault diagnosis of rotating machinery equipment. Lastly, predicated on a survey of the literature and the current developmental landscape, the challenges and prospective orientations of deep learning in rotary machinery fault diagnosis are presented.

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Section: Ganmentioning

confidence: 99%

Application of deep learning to fault diagnosis of rotating machineries

Su,

Xiang,

2024

Meas. Sci. Technol.

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“…To predict the RUL of rolling bearings with a least RMS error, Wei et al [55] introduced a novel self-adaptive graph CNN methodology by utilizing different directed graphs in multiple convolution layers in the training phase. Ghorvei et al [57] offered a comprehensive domain sub adaption graph CNN methodology for identifying cross-domain bearing defects. The proposed approach can be effectively deployed to reduce the distribution difference between and across relevant domains in latent space.…”

Section: Graph Convolutional Network (Gcns)mentioning

confidence: 99%

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

Kumar,

Gandhi,

Tang

et al. 2023

Meas. Sci. Technol.

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Health monitoring in rotatory machinery is a process of developing a mechanism to determine its state of deterioration. It involves analysing the presence of damage, locating the fault, determining the severity of the problem, and calculating the amount of time that the machine can still be used effectively by making use of signal processing methods. The journey started to repair when the machine fails and progressed to the modern era, which involves the use of advanced sensors to capture data and conduct on-line signal processing methods to extract relevant features. By seamlessly integrating advanced smart sensing, data collection, and intelligent algorithms, modern technologies have transformed the landscape of condition-based maintenance for rotary machinery, bridging the gap between fundamental understanding and practical engineering applications. In this review paper, first, the roadmap of the condition-based maintenance (CBM) journey for rotary machinery is briefly introduced. Then, CBM task techniques are reviewed in the context of manual identification of defects, applying artificial intelligence (AI) model to identify the defect in the rotary machinery, and AI to carry out defect prognosis and determine the remaining useful life. Finally, the challenges, and issues of signal processing methods to detect faults in rotary machinery, and remedies to overcome such challenges are deeply discussed and future research directions are identified to ensure safe operation for rotary machinery.

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“…At the same time, considering that neural network structure, system performance and other factors have a large impact on data processing speed and accuracy, analog methods can be used for control. The idea of the neural network algorithm is to process the information in the complex system, transform it into a simple function, and then predict and classify it [16][17].…”

Section: Figure 2 Neural Network Algorithm Modelmentioning

confidence: 99%

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network

2023

KME

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Neural network is one of the important basic knowledge in computer, chemistry and information science, microelectronics technology and other fields. It has good universality in dealing with nonlinear system problems, and is widely used in industrial automation and intelligent control. This paper mainly introduces the mathematical model, algorithm design and implementation method of the construction machinery robot arm, and studies and analyzes its kinematics. Based on the neural network and artificial neuron theory, this paper constructs the robot arm position recognition technology in the object feature database for the characteristics and requirements of the subject. Finally, the function of the robot arm control model is tested, and the test results show that the arm support of the model has a long elongation distance, which can meet the production requirements.

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Spatial graph convolutional neural network via structured subdomain adaptation and domain adversarial learning for bearing fault diagnosis

Cited by 61 publications

References 32 publications

Application of deep learning to fault diagnosis of rotating machineries

Application of deep learning to fault diagnosis of rotating machineries

Latest innovations in the field of condition-based maintenance of rotatory machinery: a review

Optimization of Control Method of Construction Machinery Manipulator Based on Neural Network

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