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Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines by Hojin Lee; Hyeyun Jeong; Gyogwon Koo; Jaepil Ban; Sang Woo Kim is a Engineering article available to read on EtoBox.

What is Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines about?

With the development of smart factories, deep learning, which automatically extracts features and diagnoses faults, has become an important approach for fault diagnosis. In this paper, a novel interturn short-circuit fault (ISCF) diagnosis approach using an attention based recurrent neural network is proposed. An encoder-decoder architecture using an attention mechanism diagnoses the ISCF by estimating a fault indicator that directly reflects the severity of the fault, using currents and rotational speed signals as inputs. The attention mechanism helps the decoding process in accurate diagnosis and solves the longterm dependency problem of the encoder-decoder structure. The proposed algorithm uses only three-phase current and rotational speed as the inputs to evaluate the severity of the ISCF and enable early stage diagnosis of ISCF. The diagnosis of ISCF is achieved in various operating points and fault conditions, and no additional sensors such as voltage and vibration sensors are required. Experimental results for various operating and fault conditions demonstrate that the proposed method effectively diagnoses ISCFs.

Who reads Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines?

Author

Hojin Lee; Hyeyun Jeong; Gyogwon Koo; Jaepil Ban; Sang Woo Kim

Publisher

IEEE; Institute of Electrical and Electronics Engineers; Institute of Electrical and Electoronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 1932-4529)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)