Early detection of PMSM faults at static and dynamic operating points by using artificial neural network for automated electric vehicle

Kilic, Ahmet; Weiss, Frank

doi:10.1007/s40430-023-04420-6

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2024

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Cited by 2 publications

References 15 publications

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Artificial Intelligence Transformation of a Permanent Magnet Synchronous Motor Via a Multiphysics-Informed Deep Operator Network

Son,

Lee,

Jeong

et al. 2024

Preprint

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Artificial Intelligence Transformation of a Permanent Magnet Synchronous Motor Via a Multiphysics-Informed Deep Operator Network

Son,

Lee,

Jeong

et al. 2024

Preprint

View full text Add to dashboard Cite

Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm

Tan,

Gong

2024

Applied Mathematics and Nonlinear Sciences

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New energy vehicles are vital in promoting environmental protection and technological innovation. Fault detection still faces challenges during its operation, and efficient and accurate methods for fault diagnosis are urgently needed. This paper proposes a fault detection and analysis model based on a decision tree algorithm for the fault detection problem of new energy vehicles. The dataset applicable to the model is prepared by preprocessing in-vehicle network data, including data cleaning, integration, and other steps. Fault prediction can be realized after using C4.5 algorithms to construct a decision tree. With a precision of 82.26% on the test set, this model is highly accurate in fault detection, which is 1.23 percentage points higher than the traditional decision tree algorithm. The model’s effectiveness and efficiency in handling large-scale data were demonstrated by its training and testing on training sets of different sizes. Using the traditional algorithm, a training set of 80,000 data was used to reduce the model’s running time from 274,432 seconds to 249,269 seconds. This study provides a practical methodology for fault diagnosis of new energy vehicles, improving fault detection accuracy while optimizing computational efficiency. Real-time monitoring and timely maintenance of new energy vehicles require this.

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Early detection of PMSM faults at static and dynamic operating points by using artificial neural network for automated electric vehicle

Cited by 2 publications

References 15 publications

Artificial Intelligence Transformation of a Permanent Magnet Synchronous Motor Via a Multiphysics-Informed Deep Operator Network

Artificial Intelligence Transformation of a Permanent Magnet Synchronous Motor Via a Multiphysics-Informed Deep Operator Network

Fault Detection and Failure Rate Analysis of New Energy Vehicles Based on Decision Tree Algorithm

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