Diagnosis of Interturn Short-Circuit Faults in Permanent Magnet Synchronous Motors Based on Few-Shot Learning Under a Federated Learning Framework

Zhang, Jinglin; Wang, Yan Bo; Zhu, Kai; Zhang, Yi; Li, Yuanjiang

doi:10.1109/tii.2021.3067915

Cited by 71 publications

(23 citation statements)

References 21 publications

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“…2 starts the estimation procedure and turns into until the estimation ends. The resulting estimated parameters are shown in the ''The estimated parameters and minimum rms value of the Error Matrix are:'' window, together with the final value ''Min_Func'' of the objective function Q in (11). The left Matlab figure shows the actual efficiency map E map act (ω m , τ e ) provided by the input Excel file.…”

Section: B Results Of the Estimationmentioning

confidence: 99%

“…As an example, the PMSMs modeling is addressed in a d-q discretetime reference frame in [6]. A modeling in the d-q frame is also employed in [7]- [10], whereas a fault diagnosis model based on a Siamese network is proposed in [11]. A fundamental advantage of model-based methods is the knowledge of the mathematical model of the system under consideration.…”

Section: Introductionmentioning

confidence: 99%

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Efficiency Map-Based PMSM Parameters Estimation Using Power-Oriented Modeling

Tebaldi

2022

IEEE Access

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This paper deals with two subjects. The first one is the presentation of a Matlab App created for PMSMs (Permanent Magnet Synchronous Motors) parameters estimation, both in the electrical and in the mechanical energetic domains. The second one is the presentation of a generalized PMSM Simulink superblock equipped with a user-friendly interface allowing to select options and to input the model parameters. The Matlab App and the PMSM superblock can be easily interfaced with each other. Finally, two simulation case studies are reported for the PMSM superblock.

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Section: B Results Of the Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficiency Map-Based PMSM Parameters Estimation Using Power-Oriented Modeling

Tebaldi

2022

IEEE Access

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“…Table 4 shows the results of the experiment. 4 that compared with other methods such as BP [10], RNN [11], SDAE [12] and PCA+SVM [13], SNSAE algorithm has a higher fault diagnosis accuracy, reaching 98.91%.…”

Section: The Comparison Of Different Methodsmentioning

confidence: 92%

Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder

Chen

Li²

2022

J. Phys.: Conf. Ser.

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Regarding the problems such as the traditional auto-encoders’ tendency to learn the similar features during the feature extraction and limited capability of feature learning in the shallow network models, the paper puts forward an interturn short circuit diagnosis method for Permanent Magnet Synchronous Motor (PMSM) based on the Stacked Normalized Sparse Autoencoder (SNSAE). The method is to guarantee the representativeness of the extracted features, acquire stronger anti-interference capability, and is more suitable for training diversified and large-capacity mechanical equipment failure data. Four types of Inter-Turn Short-Circuit (ITSC) fault data were collected on the PMSM fault diagnosis experimental platform for testing. The experimental outcome suggests that the SNSAE algorithm can effectively extract the fault features, and the fault diagnosis effect is better than the traditional intelligent diagnosis method, and its diagnosis accuracy is as high as 98.91%.

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“…In addition, CNN also integrates with FL for diagnosing in the Internet‐of‐Ships scenario, which reduced cryptography calculation and client communication times [39]. In order to identify inter‐turn short‐circuit defects, a stacked sparse Autoencoder paired with a Siamese network is presented as a solution to the limited label problem [40]. One of the most significant challenges in FL is that various clients have distinct characteristics and duties, which federated transfer learning seeks to address.…”

Section: Related Workmentioning

confidence: 99%

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Xia

Zheng

et al. 2022

IET Collab Intel Manufact

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Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.

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Diagnosis of Interturn Short-Circuit Faults in Permanent Magnet Synchronous Motors Based on Few-Shot Learning Under a Federated Learning Framework

Cited by 71 publications

References 21 publications

Efficiency Map-Based PMSM Parameters Estimation Using Power-Oriented Modeling

Efficiency Map-Based PMSM Parameters Estimation Using Power-Oriented Modeling

Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Based on Stacked Normalized Sparse Autoencoder

Privacy‐preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

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