@ARTICLE{Lale_Timur_Identification_Early,
 author={Lale, Timur and Yüksek, Gökhan},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e15158},
 howpublished={online},
 year={Early Access},
 abstract={In a very broad range of industrial applications, especially in electric vehicles, permanent magnet synchronous motors (PMSMs) play an important role. Any failure in PMSMs may cause possible safety hazards, a drop in productivity, and expensive downtime. Therefore, their reliable operation is essential. Accurate failure identification and classification allow for addressing problems before they escalate, which helps ensure the seamless operation of PMSMs and reduces the likelihood of equipment failure. Therefore, in this paper, novel failure identification methods based on gated recurrent unit (GRU) and long short-term memory (LSTM) from recurrent neural network (RNN) methods are proposed for early identification of stator interturn short circuit failure (ISCF) and demagnetization failure (DF) occurring in PMSMs under multiple operating conditions. The proposed methods use three phase current signals recorded from the experimental study under multiple operating conditions of the motor as input data. In the proposed methods, both feature extraction and classification are executed within a unified framework. The experimental outcomes obtained demonstrate that the proposed methods are able to identify a total of six unique motor conditions, including three ISCF variations and two DF variations, with high accuracy. The LSTM and GRU approaches predicted the identification of failures with 98.23% and 98.72% accuracy, respectively. Compared to existing methods, the success of the proposed approaches is satisfactory. In addition, LSTM and GRU-based failure identification methods are also compared in detail for accuracy, precision, sensitivity, specificity and training time in this study.},
 type={Article},
 title={Identification and Classification of Turn ShortCircuit and Demagnetization Failures in PMSM Using LSTM and GRU Methods},
 URL={http://czasopisma.pan.pl/Content/132548/PDF/BPASTS-04618-EA.pdf},
 keywords={gated recurrent unit (GRU), long short term memory, fault diagnosis, PMSM},
}