Applied sciences

Bulletin of the Polish Academy of Sciences Technical Sciences

Content

Bulletin of the Polish Academy of Sciences Technical Sciences | 2025 | 73 | 1

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Abstract

With a continued strong pace of artificial intelligence, the way of formulating the flight day plan has a significant impact on the efficiency of flight training. However, through extensive research, we find that the scheduling of flight days still relies on manual work in most military aviation academies. This method suffers from several issues, including protracted processing times, elevated error rates, and insufficient degree of optimization. This article provides a comprehensive analysis of automated flight scheduling using a goal programming algorithm and details the implementation of the corresponding algorithm on the LINGO platform. The study enhances the flexibility and robustness of the model by setting bias variables, wherein the flight courses for students and instructors can be automatically and reasonably scheduled.
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Authors and Affiliations

Pengfei Sun
1
ORCID: ORCID
Jia Liu
1
ORCID: ORCID
Hao Nian
1

  1. Naval Aviation University, 188 Erma Road, Yantai City, Shandong Province, China
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Abstract

In an extremely broad range of industrial applications, especially in electric vehicles, permanent magnet synchronous motors (PMSMs) play a vital 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 inter-turn 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 can 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.
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Authors and Affiliations

Timur Lale
1
ORCID: ORCID
Gökhan Yüksek
1
ORCID: ORCID

  1. Department of Electrical and Electronics Engineering, Faculty of Architecture and Engineering, Batman University,Bati Raman Campus 72000, Batman, Turkey

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