Effectiveness of RSOM neural model in detecting industrial anomalies

• Autorzy: Salhi Mohamed Salah , Barhoumi El Manaa , Lachiri Zied

Identyfikatory

DOI

10.29354/diag/146213

• Języki publikacji: EN

Abstrakty

EN

Continuous monitoring and proper diagnosis of production systems are daily concerns that involve many manufacturers. In this context, this paper proposes a feasible and effective diagnostic methodology. It is based on a recurrent dynamic neural model application, in industrial anomaly detection, with a high identification rate. The general context of this approach is summarized in the improvement of the detection and control mechanisms using intelligent systems. These tools can collaborate objectively in industrial processes diagnosis, then in anomalies detection and classification to intervene correctly. The final purpose of this paper consists in guaranteeing the operational safety for processes, ensuring their reliability and affirming the production continuity.

Słowa kluczowe

EN

RSOM model; defect diagnosis; industrial processes

PL

defekt; diagnoza; procesy przemysłowe; niezawodność; ciągłość produkcji

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2022
• Tom: Vol. 23, No. 1
• Strony: art. no. 2022106
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Salhi Mohamed Salah

• University of Tunis El Manar, National Engineering School of Tunis-Tunisia Research Laboratory of Signal Image and Information Technology LR-SITI

autor

Barhoumi El Manaa

• Dhofar University-Oman

autor

Lachiri Zied

• University of Tunis El Manar, National Engineering School of Tunis-Tunisia Research Laboratory of Signal Image and Information Technology LR-SITI

Bibliografia

• 1. Immovilli F, Bianchini C, Cocconcelli M, Bellini A, Rubini R. Bearing fault model for induction motor with externally induced vibration. IEEE Trans. ind. Electron. 2013;60(S):340S-341S. https://doi.org/10.1109/TIE.2012.2213566.
• 2. Amar M, Gondal I, Wilson C. Vibration spectrum imaging: a bearing fault classification approach. IEEE Trans. ind. Electron. 2015;62(1):494-502. https://doi.org/10.1109/TIE.2014.2327555.
• 3. Asbafkan A, Mirzaeeian B, Niroomand M, Zarchi HA. Frequency adaptive repetitive control of grid connected inverter for wind turbine applications. Electrical Engineering (ICEE). 2013 21st Iranian Conference. 2013: 13767729. https://doi.org/10.1109/IranianCEE.2013.6599846.
• 4. Rauber A, Merkl D, Dittenbach M.:The growing hierarchical selforganizing map: Exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks. 2002;13(6):1331-1341. https://doi.org/10.1109/TNN.2002.804221.
• 5. Salhi MS, Rahmouni MH, Amiri H. Evolutionary Deep-indicators Algorithm in Facial Recognition improvement over SoC. International Journal of Advanced Science and Technology. 2020;29(5): 5376-5387.
• 6. Dean J, Ghemawat S. MapReduce: simplified data processing on large clusters. Commun. ACM. 2008; 51(1):107-113. https://doi.org/10.1145/1327452.1327492.
• 7. Zhou A, Yu D, Zhang W. A research on intelligent fault diagnosis of wind turbines based on ontology and FMECA. Adv. Eng. Informatics. 2015; 29(1):115-125. https://doi.org/10.1016/j.aei.2014.10.001.
• 8. Zillner S, Ebel A, Schneider M. Towards intelligent manufacturing, semantic modelling for the steel industry. IFACPapers OnLine. 2016;49(20): 220-225. https://doi.org/10.1016/j.ifacol.2016.10.124.
• 9. Khalfaoui N, Salhi MS, Amiri H. A parallel approach for the diagnosis of electrical asynchronous training anomalies. International Journal of Applied Engineering Research (IJAER). 2017;12(9): 1836-1843.
• 10. Shadmesgaran MR, Hashimov AM, Rahmanov NR. A glance of optimal control effects on technical and economic operation in Grid. International Journal on Technical and Physical Problems of Engineering, 2021;46(13):1-10.
• 11. Tanweer A, Shamimul Q, Benaida M. Genetic Algorithm: Reviews, Implementations, and Applications. International Journal of Engineering Pedagogy. 2020;10(6). https://ssrn.com/abstract=3660827.
• 12. Achbi MS, Kechida S. Methodology for monitoring and diagnosing faults of hybrid dynamic systems: a case study on a desalination plant. Diagnostyka 2020;21(1):27–33. https://doi.org/10.29354/diag/116076.
• 13. Michalski R. (edit.). Diagnostyka maszyn roboczych. ITE Radom, 2004.
• 14. Ibrahim H, Adrian I, Rafic Y. Study of a hybrid winddiesel system with compressed air energy storage. Proceeding of the Electrical Power Conference “EPC 2007. IEEE”. 2007:01-07. https://doi.org/10.1109/EPC.2007.4520350.
• 15. Chery J, Qing L. Teaching mechatronics to nontraditional mechanical engineering students - an adaptive approach. International Journal of Engineering Pedagogy. 2021; 11(3):4-20.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).