Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence
This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the compl...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/8209 |
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| author | Sonia Ben Brahim Samia Dardouri Hanen Lajnef Amel Ben Slimane Ridha Bouallegue Tan-Hoa Vuong |
| author_facet | Sonia Ben Brahim Samia Dardouri Hanen Lajnef Amel Ben Slimane Ridha Bouallegue Tan-Hoa Vuong |
| author_sort | Sonia Ben Brahim |
| collection | DOAJ |
| description | This paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model’s accuracy, showing minimal misclassification. |
| format | Article |
| id | doaj-art-147bf3b1967d4c779339de175c9f1413 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-147bf3b1967d4c779339de175c9f14132025-08-20T02:56:51ZengMDPI AGSensors1424-82202024-12-012424820910.3390/s24248209Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial IntelligenceSonia Ben Brahim0Samia Dardouri1Hanen Lajnef2Amel Ben Slimane3Ridha Bouallegue4Tan-Hoa Vuong5InnoV’COM Laboratory-Sup’Com, University of Carthage, Ariana 2083, TunisiaDepartment of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11911, Saudi ArabiaInnoV’COM Laboratory-Sup’Com, University of Carthage, Ariana 2083, TunisiaFaculty of Computing and Information, Al Baha University, Al Baha 65526, Saudi ArabiaInnoV’COM Laboratory-Sup’Com, University of Carthage, Ariana 2083, TunisiaLAPLACE Laboratory-UMR5213, National Polytechnic Institute of Toulouse, 31077 Toulouse, FranceThis paper introduces a novel methodology for evaluating communication performance in rotating electric machines using Received Signal Strength Indication (RSSI) measurements coupled with artificial intelligence. The proposed approach focuses on assessing the quality of wireless signals in the complex, dynamic environment inside these machines, where factors like reflections, metallic surfaces, and rotational movements can significantly impact communication. RSSI is used as a key parameter to monitor real-time signal behavior, enabling a detailed analysis of communication reliability. The methodology comprises several stages, including data collection, preprocessing, feature extraction, and model training. Various machine learning models are implemented and evaluated. Among these, the SVM model with a Radial Basis Function (RBF) kernel outperforms others, achieving an accuracy of 97%, with high precision and recall scores, confirming its robustness in classifying RSSI data and handling complex signal behavior. The confusion matrix further supports the SVM model’s accuracy, showing minimal misclassification.https://www.mdpi.com/1424-8220/24/24/8209RSSIartificial intelligencerotating electrical machines |
| spellingShingle | Sonia Ben Brahim Samia Dardouri Hanen Lajnef Amel Ben Slimane Ridha Bouallegue Tan-Hoa Vuong Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence Sensors RSSI artificial intelligence rotating electrical machines |
| title | Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence |
| title_full | Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence |
| title_fullStr | Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence |
| title_full_unstemmed | Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence |
| title_short | Evaluating Communication Performance in Rotating Electrical Machines Using RSSI Measurements and Artificial Intelligence |
| title_sort | evaluating communication performance in rotating electrical machines using rssi measurements and artificial intelligence |
| topic | RSSI artificial intelligence rotating electrical machines |
| url | https://www.mdpi.com/1424-8220/24/24/8209 |
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