Prior knowledge-based self-supervised learning for intelligent bearing fault diagnosis with few fault samples

Wu, Ke; Nie, Yukang; Wu, Jun; Wang, Yuanhang

doi:10.1088/1361-6501/acddd9

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…The Seebeck coefficient theorem is a theoretical expression of the Seebeck effect, which describes how the difference in temperature between two different materials, such as metals and semiconductors, creates an electric potential difference. The theorem can be expressed as [12][13][14]:…”

Section: Temperature Measurement Principle and Basic Law Of Film Ther...mentioning

confidence: 99%

Applied to calibrate the new intelligent bearing of NiCr/NiSi film thermocouple

Liu,

Guo,

Cheng

et al. 2024

J. Phys.: Conf. Ser.

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The purpose of this paper is to study the calibration method of new intelligent bearing applied to NiCr/NiSi film thermocouple. Firstly, the basic principle and characteristics of NiCr/NiSi film thermocouple and intelligent bearing are introduced, and then the design and realization process of intelligent bearing are elaborated, including material selection, structural design, manufacturing process and so on. Then, this paper puts forward the calibration method of intelligent bearing, and verifies its effectiveness through experiments. The experimental results show that the Seebeck coefficient of NiCr/NiSi thin film thermocouple new intelligent bearing is 41.00 μV/°C, and the Seebeck coefficient of standard K thermocouple is 41.20 μV/°C. The new intelligent bearing has high sensitivity and stability, and can achieve fast response and accurate measurement. Finally, this paper summarizes the research results, and looks forward to the future research direction.

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Section: Temperature Measurement Principle and Basic Law Of Film Ther...mentioning

confidence: 99%

Applied to calibrate the new intelligent bearing of NiCr/NiSi film thermocouple

Liu,

Guo,

Cheng

et al. 2024

J. Phys.: Conf. Ser.

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show abstract

“…Despite their excellence, these methods commonly exhibit weaknesses such as poor generalization capabilities, sensitivity to adversarial examples and high algorithmic complexity, which can lead to suboptimal performance in industrial applications that demand rapid and accurate diagnostics. Recent studies have shifted focus to addressing FSL fault-diagnosis challenges through meta-learning [14][15][16], with a significant proportion of recent research in time-series signal fault diagnosis concentrating on this approach [17]. However, the practical utility of meta-learning is limited by its high dependency on data quality and distribution, which can adversely affect performance in industrial settings due to data noise and outliers [18].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fault Diagnosis in Industrial Processes through Adversarial Task Augmented Sequential Meta-Learning

Sun,

Fan,

Wang

2024

Applied Sciences

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This study introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework, designed to enhance fault diagnosis in industrial processes. ATASML integrates adversarial learning with sequential task learning to improve the model’s adaptability and robustness, facilitating precise fault identification under varied conditions. Key to ATASML’s approach is its novel use of adversarial examples and data-augmentation techniques, including noise injection and temporal warping, which extend the model’s exposure to diverse operational scenarios and fault manifestations. This enriched training environment significantly boosts the model’s ability to generalize from limited data, a critical advantage in industrial applications where anomaly patterns frequently vary. The framework’s performance was rigorously evaluated on two benchmark datasets: the Tennessee Eastman Process (TEP) and the Skoltech Anomaly Benchmark (SKAB), which are representative of complex industrial systems. The results indicate that ATASML outperforms conventional meta-learning models, particularly in scenarios characterized by few-shot learning requirements. Notably, ATASML demonstrated superior accuracy and F1 scores, validating its effectiveness in enhancing fault-diagnosis capabilities. Furthermore, ATASML’s strategic incorporation of task sequencing and adversarial tasks optimizes the training process, which not only refines learning outcomes but also improves computational efficiency. This study confirms the utility of the ATASML framework in significantly enhancing the accuracy and reliability of fault-diagnosis systems under diverse and challenging conditions prevalent in industrial processes.

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“…Fortunately, the introduction of meta-learning has simultaneously tackled the challenges of data scarcity and cross-domain, leading to satisfactory results in handling FSFD tasks [11]. Current applications in fault diagnosis are mainly in the areas of parameter optimization [12,13], similarity metrics [14][15][16], and modelbased [17] methods. Among them, the metric-based methods are widely adopted because of their stronger generalization capability and portability.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical transformer-based adaptive metric and joint-learning network for few-shot rolling bearing fault diagnosis

Meng,

Zhang,

Guan

et al. 2023

Meas. Sci. Technol.

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Recently, deep learning techniques have significantly bolstered the advancement of intelligent fault diagnosis. However, in engineering practice, the limited availability of fault samples poses considerable challenges to the existing methods. To address this problem, a hierarchical Transformer-based adaptive metric and joint-learning network (HTAMJN) is suggested in this paper. Firstly, a hierarchical progressive fusion encoder based on orthogonal self-attention is devised, which effectively enhances the model’s attention to local features and establishes comprehensive interdependencies. To reduce the demand for labeled data, a metric method with a coupling mechanism is proposed to adaptively enhance the metric benchmark, making the category representation more discriminative. Additionally, a joint optimization scheme based on the episodic paradigm is formulated to improve the recognition capability of the network for different categories under few-shot scenarios. The outcomes demonstrate that the HTAMJN outperforms several comparison models in both few-shot diagnosis and cross-domain recognition tasks in experiments conducted on three bearing datasets, exhibiting superior performance.

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Prior knowledge-based self-supervised learning for intelligent bearing fault diagnosis with few fault samples

Cited by 7 publications

References 46 publications

Applied to calibrate the new intelligent bearing of NiCr/NiSi film thermocouple

Applied to calibrate the new intelligent bearing of NiCr/NiSi film thermocouple

Enhancing Fault Diagnosis in Industrial Processes through Adversarial Task Augmented Sequential Meta-Learning

A hierarchical transformer-based adaptive metric and joint-learning network for few-shot rolling bearing fault diagnosis

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