Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation

Shao, Haidong; Li, Wei; Cai, Baoping; Xiao, Yiming; Yan, Shen

doi:10.1109/tii.2022.3232766

Cited by 130 publications

(48 citation statements)

References 28 publications

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“…Gears are prone to wear, fracture, and other failures due to long-term work in complex and terrible environments, cause the machine to malfunction and resulting in incalculable economic losses (Shi et al, 2022; Xiao et al, 2022). Therefore, carrying out research on fault diagnosis and state detection of gears has long-term significance for ensuring the safe operation of mechanical equipment (Shao et al, 2023; Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

Han

Du³

et al. 2023

Transactions of the Institute of Measurement and Control

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In recent years, transfer learning has been widely used in mechanical fault diagnosis with some achievements. However, most transfer learning methods do not perform well in diagnosis when the speed and load change simultaneously. Inspired by the adversarial learning mechanism, a transfer learning method named attention mechanism-guided domain adversarial network (AMDAN) is proposed in this paper. AMDAN regards the convolutional neural networks (CNNs) as the generator of the domain adversarial network to learn mutually invariant features and the domain classifier as the discriminator of the domain adversarial network. Attention mechanism is introduced to take into account the interchannel and intraspace feature fusion to improve the training efficiency. Then, multi-kernel maximum mean discrepancy (MK-MMD) is used to measure the distance of different feature spaces to achieve domain alignment. Finally, the superiority of AMDAN is verified by two sets of gear fault diagnosis experiments. The experimental results show that AMDAN has the highest classification accuracy and the strongest generalization ability compared with other methods.

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Section: Introductionmentioning

confidence: 99%

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

Han

Du³

et al. 2023

Transactions of the Institute of Measurement and Control

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“…The proposed method generates a feature attribute matrix for a given dataset X = ( X 1 , X 2 , …, X n ) T consisting of n data samples, each with d-dimensional attribute values X i = { x i 1 , x i 2 , …, x id } For instance, missing data on a 4*5 scale as shown in Fig. 3 can be represented as X = ( X 1 , X 2 , X 3 , X 4 , X 5 ) T where “ x ij ” represents observable data, and “/” represents missing data [22].…”

Section: Introductionmentioning

confidence: 99%

Cardiac disease diagnosis based on GAN in case of missing data

Chen,

Zhang,

Yang

et al. 2023

Preprint

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In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. Additionally, data integration is difficult and poses privacy protection challenges, resulting in a large number of missing data in the dataset. The presence of incomplete data significantly reduces the quality of the published data, hindering the timely analysis of data and the generation of reliable knowledge by epidemiologists, public health authorities, and researchers.Consequently, this affects the downstream tasks that rely on this data. To address the issue of discrete missing data in cardiac disease, this paper proposes the AGAN(Attribute Generative Adversarial Nets) architecture for missing data filling, based ongenerative adversarial networks. This algorithm takes advantage of the strong learning ability of generative adversarial networks. Given the ambiguous meaning of filling data in other network structures, the attribute matrix is designed to directly convert it into the corresponding data type, making the actual meaning of the filling data more evident. Furthermore, the distribution deviation between the generated data and the real data is integrated into the loss function of the generative adversarial networks,improving their training stability and ensuring consistency between the generated data and the real data distribution. This approach establishes the missing data filling mechanism based on the generative adversarial networks, which ensures the rationality of the data distribution while filling the missing data samples. The experimental results demonstrate that compared to other filling algorithms, the data matrix filled by the proposed algorithm in this paper has more evident practical significance, fewer errors, and higher accuracy in downstream classification prediction.

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“…By establishing a relationship between key and process variables within the available data, data-driven models can be used to predict critical product quality in the process industry (Chen et al, 2022). Data-driven models, such as latent variable models (LVMs) (including principal component analysis (PCA) (Liang et al, 2021) and partial least squares (PLS) (Chiplunkar and Huang, 2019), artificial neural networks (ANNs) (Kazmi et al, 2023), generative adversarial network (GAN) (Shao et al, 2023), Gaussian process regression (GPR) (Deringer et al, 2021) and support vector machines (SVMs) (Wu and Faisal, 2020)), have been widely used in various process controls. Although these methods are effective, they often treat measurement data as discrete samples and analyse them using multivariate statistical methods, which can overlook the continuous characteristics of process variables.…”

Section: Introductionmentioning

confidence: 99%

A data-driven soft-sensing approach using probabilistic latent variable model with functional data framework

Tan

Guo

Liu

et al. 2023

Transactions of the Institute of Measurement and Control

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Functional principal component analysis (FPCA) and functional partial least squares (FPLS) are two mainstream functional data analysis (FDA) methods, which have been commonly used to extract deep information hidden in the original data space. However, the process data always contain random noise, which affects the performance of FDA models. To overcome this issue, two functional probabilistic latent variable models (FPLVMs), including functional probabilistic principal component analysis (FPPCA) and functional probabilistic partial least squares (FPPLS) are proposed in this work. First, the process data are converted into functional data using the FDA. Subsequently, a log-likelihood function considering the noise factor and functional latent variables is designed. Finally, the regression model parameters are estimated using an expectation–maximisation algorithm. In contrast to FPPCA, FPPLS decomposes the process data and the key variable with constrained latent variables, which is similar to the partial least squares (PLS) and the principal component analysis (PCA). Moreover, the degeneration mechanism from FPLVMs into probabilistic latent variable models and latent variable models is discussed. An adaptive strategy with functional covariance is used to satisfy the online predictive capabilities of the model. Finally, the proposed approach is validated using a numerical case, the Tennessee Eastman process and an industrial o-xylene distillation column for evaluation.

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Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation

Cited by 130 publications

References 28 publications

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

Cardiac disease diagnosis based on GAN in case of missing data

A data-driven soft-sensing approach using probabilistic latent variable model with functional data framework

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