Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network

Liang, Ping; Yu, Zhibin; Wang, Bin; Xu, Xuefang; Tian, Junrui

doi:10.1016/j.aei.2023.102075

Cited by 52 publications

(6 citation statements)

References 41 publications

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“…The sample data are defined as the source domain data set, and the target domain data set is obtained from the measured data [18]. By extracting data from the target domain and the source domain data, the mapping function from the measured data to the sample data is established for fault classification and identification [19]. TL can be classified according to the target domain label and the migration method, as displayed in Figure 4.…”

Section: Modern Intelligent Fault Diagnosis Methodsmentioning

confidence: 99%

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Zhu,

Wu,

Tang

et al. 2023

JMSE

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As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.

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Section: Modern Intelligent Fault Diagnosis Methodsmentioning

confidence: 99%

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Zhu,

Wu,

Tang

et al. 2023

JMSE

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show abstract

“…This approach harnessed the potent feature extraction capabilities of the Transformer and employed contrastive learning principles to achieve transfer fault diagnosis. Liang et al [175] achieved transfer fault diagnosis in variable operating conditions by fusing subdomain adaptation with an enhanced visual transformer. Their method replaced plain counterparts with deformable convolution modules and employed RNN-based adaptive position encoding.…”

Section: Transformermentioning

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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“…The receptive field of CNN filters is limited, and using RNN, it is difficult to effectively describe the long-range correlations. 36 To tackle these problems, an emerging deep model structure called Transformer is proposed, which has been effectively applied in natural language processing tasks. 37 The attention mechanism is a key component in the Transformer model, which can describe the interaction relationship between entities in the sequence.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A novel minority sample fault diagnosis method based on multisource data enhancement

Guo,

Song,

Huang

2024

Int Journal of Mech Sys Dyn

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Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems. However, the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions. To address this challenge, this paper proposes a novel deep‐learning model for multisource data augmentation and small sample fault diagnosis. The raw multisource data are first converted into two‐dimensional images using the Gramian Angular Field, and a generator is built to transform random noise into images through transposed convolution operations. Then, two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability. The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator. Furthermore, a global optimization strategy is designed to upgrade parameters in the model. The discriminators and generator compete with each other until Nash equilibrium is achieved. A real‐world multistep forging machine is adopted to compare and validate the performance of different methods. The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities. Compared with other state‐of‐the‐art models, the proposed approach has better fault diagnosis accuracy in various scenarios.

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Fault transfer diagnosis of rolling bearings across multiple working conditions via subdomain adaptation and improved vision transformer network

Cited by 52 publications

References 41 publications

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

Application of deep learning to fault diagnosis of rotating machineries

A novel minority sample fault diagnosis method based on multisource data enhancement

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