Improved few-shot learning method for transformer fault diagnosis based on approximation space and belief functions

Xu, Yao-Yu; Li, Yuan; Wang, Yijing; Zhong, Dexing; Zhang, Guanjun

doi:10.1016/j.eswa.2020.114105

Cited by 23 publications

(13 citation statements)

References 35 publications

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“…Bearing failure is caused by the environment of long-term high-speed operation, which will affect equipment safety and cause property losses (Chen et al, 2020a; Luo et al, 2019; Wu et al, 2019; Zhang et al, 2020). Therefore, it is of great engineering value to monitor and diagnose the working state of bearings (Chen et al, 2022; Fang and Wu, 2021; Tang et al, 2022; Xu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on variational mode decomposition optimized by CS-PSO

Liu¹,

Wang²,

Changwei³

et al. 2023

Journal of Vibration and Control

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In order to solve the problem that bearing vibration signal fault feature is difficult to extract effectively under noise, a fault diagnosis method based on Variational Mode Decomposition (VMD) optimized by Cuckoo Search (CS) and Particle Swarm Optimization (PSO) is proposed. The effect of VMD is affected by the number of modes and the penalty parameter. The Levy flight strategy and elimination mechanism of the CS algorithm is added to PSO algorithm and the position updating process is optimized. According to the correlation coefficient and envelope entropy of the Intrinsic Mode Function (IMF), the objective function is constructed to search for the optimal combination of VMD mode number and penalty parameter. The bearing fault type is determined by analyzing the IMF envelope spectrum of the optimal objective function. The fault diagnosis classification task upon the bearing sample data from Case Western Reserve University demonstrates that the proposed CS-PSO algorithm improves the model’s classification performance by 5%, which is on average 7.3% higher than other state-of-the-art models. This model can provide an important reference for the accuracy of bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on variational mode decomposition optimized by CS-PSO

Liu¹,

Wang²,

Changwei³

et al. 2023

Journal of Vibration and Control

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“…Based on dissolved gas analysis (DGA), more scholars adopted the artificial neural network (ANN), the polynomial neural network (PNN), etc., [ 19 , 20 , 21 ]. Moreover, some studies have optimized the algorithms to achieve better results [ 22 , 23 , 24 ]. Compared to the three-ratio method, the adoption of machine learning significantly improved the accuracy of diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Tan

Zhang

et al. 2022

Sensors

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Partial discharge (PD) is a common phenomenon of insulation aging in air-insulated switchgear and will change the gas composition in the equipment. However, it is still a challenge to diagnose and identify the defect types of PD. This paper conducts enclosed experiments based on gas sensors to obtain the concentration data of the characteristic gases CO, NO2, and O3 under four typical defects. The random forest algorithm with grid search optimization is used for fault identification to explore a method of identifying defect types through gas concentration. The results show that the gases concentration variations do have statistical characteristics, and the RF algorithm can achieve high accuracy in prediction. The combination of a sensor and a machine learning algorithm provides the gas component analysis method a way to diagnose PD in an air-insulated switchgear.

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“…The essence of these methods is to achieve efficient few-shot learning by acquiring meta-knowledge. However, the current understanding of meta-knowledge is not deep enough, so the generalization ability of related meta-learning needs to be improved [19]. However, in few-shot learning research, another very effective method is often overlooked, which is the method of knowledge reasoning.…”

Section: Introductionmentioning

confidence: 99%

Generative knowledge-based transfer learning for few-shot health condition estimation

Kang

Xiao

Xue

2022

Complex Intell. Syst.

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In the field of high-end manufacturing, it is valuable to study few-shot health condition estimation. Although transfer learning and other methods have effectively improved the ability of few-shot learning, they still cannot solve the lack of prior knowledge. In this paper, by combining data enhancement, knowledge reasoning, and transfer learning, a generative knowledge-based transfer learning model is proposed to achieve few-shot health condition estimation. First, with the effectiveness of data enhancement on machine learning, a novel batch monotonic generative adversarial network (BM-GAN) is designed for few-shot health condition data generation, which can solve the problem of insufficient data and generate simulated training data. Second, a generative knowledge-based transfer learning model is proposed with the performance advantages of the belief rule base (BRB) method on few-shot learning, which combines expert knowledge and simulated training data to obtain a generalized BRB model and then fine-tunes the generalized model with real data to obtain a dedicated BRB model. Third, through uniform sampling of NASA lithium battery data and simulating few-shot conditions, the generative transfer-belief rule base (GT-BRB) method proposed in this paper is verified to be feasible for few-shot health condition estimation and improves the estimation accuracy of the BRB method by approximately 17.3%.

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Improved few-shot learning method for transformer fault diagnosis based on approximation space and belief functions

Cited by 23 publications

References 35 publications

Bearing fault diagnosis method based on variational mode decomposition optimized by CS-PSO

Bearing fault diagnosis method based on variational mode decomposition optimized by CS-PSO

Diagnosis of Partial Discharge Based on the Air Components for the 10 kV Air-Insulated Switchgear

Generative knowledge-based transfer learning for few-shot health condition estimation

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