Self-paced decentralized federated transfer framework for rotating machinery fault diagnosis with multiple domains

Zhao, Ke; Liu, Zhenbao; Li, Jia; Zhao, Bo; Jia, Zhen; Shao, Haidong

doi:10.1016/j.ymssp.2024.111258

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…Federated learning techniques allow model training in a distributed data environment, protecting data privacy while improving the generalisation ability of the model. For example, the self-paced decentralised federated transfer framework combines federated learning and transfer learning [11]. The self-paced nature of the data is taken into account while enabling model training and transfer in distributed data environments, which improves the generalisation ability and adaptability of the model.…”

Section: Introductionmentioning

confidence: 99%

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Chen,

Zhao,

Zhang

et al. 2024

Eng. Res. Express

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As a key component of mechanical equipment, real-time monitoring and diagnosis of rolling bearings play a critical role in ensuring the stable operation of equipment and the safety of operators. In order to present the current status and trends of fault diagnosis research on rolling bearings more intuitively, the scientific knowledge mapping was used to visualize and analyze the relevant literature in the article. The results show that the number of publications in this area of research has grown significantly in recent years, with China, India, the United States, and England having contributed significantly. The journals such as MECHANICAL SYSTEMS AND SIGNAL PROCESSING, MEASUREMENT, and JOURNAL OF SOUND AND VIBRATION have played an important role in disseminating cutting-edge technologies in this field. In addition, the exploration of modern methods based on data-driven and artificial intelligence, as well as their application to real-world problems, are gradually becoming the focus of research. Through summarising and analysing, the application of modern data processing techniques, the development of more efficient and practical intelligent fault diagnosis techniques, and the close integration of laboratory research and practical applications will become future research trends.

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Section: Introductionmentioning

confidence: 99%

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Chen,

Zhao,

Zhang

et al. 2024

Eng. Res. Express

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“…However, detecting abnormal noises or subtle faults in bearings can be challenging, making it difficult to mitigate potential risks [ 3 , 4 ]. Minor issues may lead to equipment malfunction or downtime, resulting in economic losses, while more serious failures can pose catastrophic safety hazards [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis

Lu,

Liu,

Gong

et al. 2024

Sensors

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Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately addresses fault diagnosis issues across devices. To better balance the relationship between computational resources and diagnostic accuracy, a knowledge distillation-based lightweight transfer learning framework for rolling bearing diagnosis is proposed in this study. Specifically, a deep teacher–student model based on variable-scale residual networks is constructed to learn domain-invariant features relevant to fault classification within both the source and target domain data. Subsequently, a knowledge distillation framework incorporating a temperature factor is established to transfer fault features learned by the large teacher model in the source domain to the smaller student model, thereby reducing computational and parameter overhead. Finally, a multi-kernel domain adaptation method is employed to capture the feature probability distribution distance of fault characteristics between the source and target domains in Reproducing Kernel Hilbert Space (RKHS), and domain-invariant features are learned by minimizing the distribution distance between them. The effectiveness and applicability of the proposed method in situations of incomplete data across device types were validated through two engineering cases, spanning device models and transitioning from laboratory equipment to real-world operational devices.

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“…With the evolution of deep learning, numerous fault diagnosis methods have emerged for automatically detecting bearing faults [11,12]. Deep learning models' network layers enable hierarchical feature learning for bearing fault diagnosis [13].…”

Section: Introductionmentioning

confidence: 99%

An intelligent compound fault diagnosis method using generalized zero-shot model of bearing

Cen,

Zhao,

Liu

et al. 2024

Meas. Sci. Technol.

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Compound fault occurrence has been unpredictable, especially in industrial scenarios where it is difficult to collect a large number of labeled samples for compound fault. Based on this, this paper proposes a generative generalized zero-shot learning (GZSL) model aimed at synthesizing compound fault features through training with single fault samples. These synthesized features are then used for the recognition of compound fault. Firstly, in order to construct an accurate and effective semantic vector, the semantic generation module and discriminator are utilized to generate the semantics of compound fault. Secondly, a feature extraction module based on CNN is designed to extract various fault features from the two-dimensional time-frequency diagram. Finally, a fault semantic matching module is designed to match the feature vectors of compound faults with the generated fault semantic vectors. This enables the identification of unseen compound fault by computing their maximum similarity. The experimental results demonstrate that the proposed method achieved H scores of 75.83 and 69.24 on two real fault datasets, ensuring the correct classification of compound fault to the greatest extent possible.

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Self-paced decentralized federated transfer framework for rotating machinery fault diagnosis with multiple domains

Cited by 10 publications

References 28 publications

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Lightweight Knowledge Distillation-Based Transfer Learning Framework for Rolling Bearing Fault Diagnosis

An intelligent compound fault diagnosis method using generalized zero-shot model of bearing

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