Mahmoud Sayed Mahmoud Eid

• Nguyen, Thien Phuoc; Eid, Mahmoud Sayed Mahmoud; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2026). Physics-Guided Cycle-Consistency Training for Stator Winding Fault Diagnosis in Synchronous Generators without Experimental Fault Data. IEEE Transactions on Transportation Electrification. ISSN 2577-4212. doi: 10.1109/TTE.2026.3727756. Fulltekst i vitenarkiv Vis sammendrag

  Stator inter-turn short circuit faults in shipboard synchronous generators (SGs) can rapidly escalate from minor insulation degradation into catastrophic failures, threatening the operational continuity and safety of electrified vessels. Therefore, early fault severity estimation of SGs is essential to prevent extensive downtime and ensure the resilience of shipboard power systems. However, the conventional data-driven approaches typically require labeled faulty data, which are rarely obtainable in practical maritime operations. To address this limitation, this article proposes a machine learning framework that enables early fault severity estimation without the need for experimental fault samples for training. The proposed method couples two neural networks through a physics-guided cycle-consistency (PGCC) training strategy. This approach facilitates both high-fidelity synthetic data generation and domain adaptation from analytical simulations to experimental measurements. The framework is validated on an in-house 7.5 kW test bench, demonstrating that the proposed method achieves high-accuracy prediction of unseen experimental fault severities. Ablation studies confirm the contribution of each network component and the PGCC training strategy. Furthermore, a comparative study demonstrates that the proposed method outperforms the tested domain adaptation and generative adversarial networks-based methods, particularly under limited training data.

• Eid, Mahmoud Sayed Mahmoud; Ali, Ahmed Mohamed Salem; Huynh, Khang & Robbersmyr, Kjell Gunnar (2025). Robust Self-augmented Open-circuit Fault Diagnosis of Three-level Inverters for EV Powertrains. IEEE Transactions on Transportation Electrification. ISSN 2577-4212. 11(4), s. 10250–10261. doi: 10.1109/TTE.2025.3554526. Fulltekst i vitenarkiv Vis sammendrag

  With the rapid advancement of data collection systems, data-driven fault diagnosis approaches have significantly improved the detection of open-circuit (OC) faults in voltage source inverters (VSIs). However, the diverse operating conditions and noisy environments in electric vehicles (EVs) cause feature variations, rendering challenges in maintaining the high accuracy and robustness of existing methods. This article introduces a robust self-augmented deep learning (DL) method for detecting OC faults in VSIs working at various operation conditions. The proposed method automatically extracts the most relevant statistical features from the measured signals using an automatic feature extraction module (AFEM) based on an improved particle swarm optimization (PSO) algorithm. Then, these optimized features are utilized through an attention-based multi-input convolution neural network (A-MICNN) embedded with a multihead self-attention module (SAM) to effectively capture the local and global characteristics of different OC faults. The A-MICNN architecture effectively processes these multi-inputs by employing shared weights and feature extraction layers to fuse information from statistical features and time-frequency image data. The proposed method is validated using an in-house three-level (3L) F-type inverter setup, achieving a remarkable diagnosis accuracy of 99.74% with high robustness across various speeds, load conditions, and noise levels, providing an efficient solution for EV powertrain fault diagnosis.

• Laursen, Mikal; Huynh, Van-Van; Ha, Duy-Hung; Eid, Mahmoud Sayed Mahmoud & Huynh, van Khang (2025). Robust Fault Classification in Permanent Magnet Synchronous Machines Under Dynamic and Noisy Conditions. IEEE Access. 13, s. 115217–115225. doi: 10.1109/ACCESS.2025.3585518. Fulltekst i vitenarkiv Vis sammendrag

  Detection and isolation of multiple low-severity faults in permanent magnet synchronous machines (PMSMs) under dynamics and noisy conditions are very important to enhance the system’s reliability, lifetime, and service availability. This study investigates a robust fault classification scheme for PMSMs under low-severity, multiple-fault conditions in noisy and variable-speed scenarios. Two supervised learning models, namely Extra Trees (ET) and Support Vector Machine (SVM), are developed and compared using time-domain (TD) and frequency-domain (FD) features. A hybrid feature extraction method is adopted to balance the computational burden and classification accuracy. Hyperparameters and frequency-domain (FD) features are optimized based on prediction confidence, prediction times, and accuracy. Furthermore, the robustness of the data-driven models is evaluated at different Signal-to-Noise (SNR) ratios under mixed faults and variable speeds in an in-house PMSM, combining an inter-turn short circuit fault of 2.2% in one phase and a 30% demagnetization of one rotor magnet. A comparative study was conducted, confirming the proposed ET model’s superior performance in terms of prediction accuracy and robustness.

• Eid, Mahmoud Sayed Mahmoud; Ali, Ahmed Mohamed Salem; Huynh, Khang; Robbersmyr, Kjell Gunnar & Pomarnacki, Raimondas (2024). Highly Accurate Diagnosis Scheme of Open-Circuit Faults in a Three-Level Inverter Using Optimized Multi-Classifier Decision Fusion, 2023 24th International Middle East Power System Conference (MEPCON). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350358469. doi: 10.1109/MEPCON58725.2023.10462369. Fulltekst i vitenarkiv
• Eid, Mahmoud Sayed Mahmoud; Ali, Ahmed Mohamed Salem; Huynh, van Khang & Robbersmyr, Kjell Gunnar (2024). A Few–Shot Open-Circuit Fault Diagnosis of F-Type Inverters Using CGAN-Based Vision Transformer. IEEE Journal of Emerging and Selected Topics in Power Electronics. ISSN 2168-6777. 13. doi: 10.1109/JESTPE.2024.3478378. Fulltekst i vitenarkiv Vis sammendrag

  Multilevel inverters (MLIs) are widely adopted in various industries due to their distinctive features. However, they are susceptible to higher failure rates due to the increased number of components. Deep learning (DL) models are widely used for accurately diagnosing faults in inverters because they effectively extract features automatically. These models work on the hypothesis of the availability of a sufficient number of samples to train the diagnostic models. However, obtaining enough sample data in engineering practice is difficult, especially in fault cases. Therefore, this article proposes a fault diagnosis scheme combining a conditional generative adversarial network (CGAN) and a vision transformer (ViT) for diagnosing opencircuit (OC) faults with few fault samples (few shots). First, the measured signals are converted to time–frequency images. Afterward, CGAN generates new 2-D sample images with data distributions similar to real samples. Finally, the improved ViT uses the original and generated samples to learn and extract local and global features with a multihead self-attention mechanism and classify the faults. The proposed scheme is validated using an experimental setup of the F-type inverter, and the results show that the suggested scheme outperforms the other conventional DL methods with an accuracy of 98.46% using only six samples per class.

• Eid, Mahmoud Sayed Mahmoud; Huynh, Khang; Senanayaka, Jagath Sri Lal; Robbersmyr, Kjell Gunnar & Panadero, Ruben (2023). A two-stage Fault Diagnosis Scheme of Partial Demagnetization Faults in PMSMs with Low Labelled Rates, 2023 IEEE International Electric Machines & Drives Conference (IEMDC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350398991. doi: 10.1109/IEMDC55163.2023.10238950. Fulltekst i vitenarkiv
• Eid, Mahmoud Sayed Mahmoud; Huynh, Khang; Senanayaka, Jagath Sri Lal & Robbersmyr, Kjell Gunnar (2023). Robust Multiple-Fault Diagnosis of PMSM Drives Under Variant Operations and Noisy Conditions. IEEE Open Journal of the Industrial Electronics Society (OJ-IES). 4, s. 762–772. doi: 10.1109/OJIES.2024.3350443. Fulltekst i vitenarkiv Vis sammendrag

  With the rapid development of industrial applications using permanent magnet synchronous motors (PMSMs) and the Internet of Things, the demand for using robust fault diagnosis methods working in noisy conditions has increased significantly. The current data-driven methods depend mainly on deep learning (DL) models due to the effectiveness of automated feature extraction. However, these models have shallow depths compared with benchmark convolution neural networks, limiting their accuracy in final predictions, and they are established based on the hypothesis that the measured data are noiseless. Despite this, electric machinery is subjected to various noise sources that interfere with measurements during operation. This article proposes a new scheme combining a transfer-learned pretrained residual neural network (ResNet) and supervised machine learning (S-ML) to enhance the performance of DL models in noisy industrial environments. The effectiveness of the proposed scheme is validated using an in-house setup of a PMSM drive with demagnetization and intern-short circuit faults at variant operating conditions. The results show that the proposed method significantly reduced the computational burden by tenfold on average while improving the average accuracy to 96.84% across all the datasets compared with other DL and S-ML methods, with high robustness in noisy working conditions.

• Eid, Mahmoud Sayed Mahmoud; Huynh, Van Khang; Senanayaka, Jagath Sri Lal & Panadero, Ruben (2022). Fault Diagnosis of Low-severity Demagnetization in Permanent Magnet Synchronous Motors Using Numerical Data, 978-1-7281-6117-4. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728161174. doi: 10.1109/ICEMS56177.2022.9983411. Fulltekst i vitenarkiv

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• Eid, Mahmoud Sayed Mahmoud; Huynh, van Khang; Senanayaka, Jagath Sri Lal; Robbersmyr, Kjell Gunnar & Panadero, Ruben (2023). A two-stage Fault Diagnosis Scheme of Partial Demagnetization Faults in PMSMs with Low Labelled Rates. Fulltekst i vitenarkiv
• Eid, Mahmoud Sayed Mahmoud (2025). Data-driven Models for Robust Fault Diagnosis of Inverter-fed Permanent Magnet Synchronous Motors. Universitetet i Agder. ISSN 9788284272894. Fulltekst i vitenarkiv Vis sammendrag

  Med den raske utviklingen av digitaliseringen av transport- og energisystemer blir smarte sensorer og sammenkoblede plattformer blir nå sanntids tilstandsovervåking (CM) og prediktivt vedlikehold muliggjort. Dette paradigmeskiftet transformerer vedlikeholdsstrategier fra reaktive reparasjoner til proaktive tiltak, noe som forbedrer systempåliteligheten og oppfyller de strenge sikkerhetskravene i Industri 4.0. Permanentmagnet-synkronmotorer (PMSM) har etablert seg som det foretrukne valget for moderne elektriske drivsystemer – særlig i store vindturbiner og fremdriftsapplikasjoner – på grunn av deres høye virkningsgrad, momenttetthet og dynamiske respons. For å kunne drifte disse motorene effektivt benyttes spenningskildeomformere, spesielt topologier med tre nivåer, i stor grad for å levere vekselstrøms-bølger av høy kvalitet med lav harmonisk forvrengning, samtidig som de muliggjør presis regulering av hastighet og moment. Til tross for disse fremskrittene møter eksisterende metoder for feildiagnostikk fortsatt betydelige utfordringer som følge av varierende driftsforhold, tilstedeværelse av flere samtidige feil og støyende omgivelser – faktorer som alle begrenser den diagnostiske nøyaktigheten. I tillegg reduserer mangelen på historiske feildata i praktiske anvendelser effektiviteten til datadrevne metoder, som vanligvis krever store mengder merkede treningsdata. Å håndtere disse utfordringene har derfor blitt et sentralt forskningsfokus innen feltet for feiloppdagelse og -diagnostikk (FDD). Denne avhandlingen presenterer seks nye data-drevne FDD-modeller som er spesifikt utviklet for omformer-matede PMSM-er. Modellene er utviklet med tre hovedmål: Det første er å forbedre diagnosepresisjonen for flere typer feil under varierende driftsforhold, andre er å styrke robustheten til modellene i støyende miljøer, og til slutt å oppnå pålitelig feildiagnostikk selv når kun et begrenset antall feildata er tilgjengelig. De foreslåtte modellene er teoretisk beskrevet og validert gjennom en kombinasjon av endelige element analysemodeller, eksperimentelle oppsett og sanntids hardware-in-the-loop (HIL)-eksperimenter. De eksperimentelle resultatene viser at de seks modellene samlet oppnår robusthet og effektiv FDD-ytelse med lav beregningsbelastning, noe som gjør dem godt egnet for implementering i prediktive vedlikeholdsstrategier for PMSM-drifter. Dette bidrar direkte til å støtte kravene til pålitelighet, effektivitet og sikkerhet i smarte industrielle applikasjoner. For ytterligere å validere de foreslåtte tilnærmingene er det gjennomført sammenlignende studier med andre data-drevne metoder, som demonstrerer den overlegne ytelsen til de utviklede metodene. En kort oppsummering av modellene og deres hovedfunn presenteres i denne avhandlingen, mens detaljerte forklaringer og resultater gis i de vedlagte artiklene.

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