Emilia Maria CAMPEAN, Claudiu Ioan ABRUDAN, Mircea Cornel ARION


Digitalization of industrial activities assures a higher production volume and the exploitation in optimal conditions with high performance of industrial systems. These objectives are related with preventing malfunctions caused by faulty equipment. Industrial system digitalization combines the equipment with facilities like: IoT, Machine Learning or Big Data. Accidental machinery failure can be eliminated with the help of new technologies. Fault diagnosis and monitoring conditions have been studied aiming to prevent the occurrence of industrial installations interruption due to engine failure. The paper analysis the trends of industrial maintenance and real-time identification of possible defects in the beginning state of wear. Its analysis the monitor and faults diagnosis of electric motor to increase operational safety using the vibration analysis method.

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Jan Lipus, Robert Jankovych Milos Hammer, Tadeas Lipus, Vibration and related diagnostics of motors and generators, Brno University of Technology Faculty of Mechanical Engineering Institute of Production Machines, Systems and Robotics DOI : 10.17973/MMSJ.2016_12_2016202, ISSN 1805-0476, MM Maschinenmarkt Czech Republic, 2016.

Mohamad Hazwan Mohd Ghazali and Wan Rahiman, Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review, Hindawi Shock and Vibration, Article ID 9469318, ISSN: 1875-9203, Londra, Marea Britanie, 25 pages, 2021.

W.-B. Zoungrana, A. Chehri, and A. Zimmermann, Automatic classification of rotating machinery defects using machine learning (ml) algorithms, Human Centred Intelligent Systems, vol.189, ISSN 2667-1336, Split, Croatia, pp.193–203, 2020.

Ovidiu CHIRIBĂU, Cornel CIUPAN, Monitoring and analysis of a CNC turning lathe machine vibration - case study, Acta Technica Napocensis Series: Applied Mathematics and Mechanics, Vol. 55, Issue IV, ISSN 1221 – 5872, Cluj Napoca, Romania, 2012.

Forsthoffer, Michael S., Forsthoffer's More Best Practices for Rotating Equipment,, Publisher: Butterworth-Heinemann, ISBN 10: 0128092777, U.S.A., 2017.

G.Betta, C.Liguori, A. Paolillo, and A.Pietrosanto, A DSP based FFT - analyzer for the fault diagnosis of rotating machine based on vibration analysis, IEEE Transactions on Instrumentation and Measurement, ISSN: 1557-9662, vol.51,no.6,pp.1316–1322, 2002.

W E Forsthoffer, Best Practices for Rotating Machinery, Elsevier Ltd, ISBN: 9780080966779, 2011.

Kahiomba Sonia Kiangala & Zenghui Wang, Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts, The International Journal of Advanced Manufacturing Technology volume 97, ISSN: 2278-8735, pages 3251–3271, 2018.

B.S. Dhillon, Engineering maintenance – A modern approach, Florida: CRC Press, ISSN 9780429132209, 2002.

Donato Catenazzo, Brendan O' Flynn, Michael Walsh, On the use of Wireless Sensor Networks in Preventative Maintenance for Industry 4.0, 2018 Twelfth International Conference on Sensing Technology (ICST), Limerick, Ireland, ISBN:978-1-5386-5147-6, 2018.

Fink O, Wang Q, Svensen M, Dersin P, Lee W-J, Ducoffe ´ M, Potential, challenges and future directions for deep learning in prognostics and health management applications. Eng Appl Artif Intell 92:103678. ISSN 0952-1976., 2020.

Fawaz HI, Forestier G, Weber J, Idoumghar L, Muller PA, Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963. ISSN 1573756X., 2019.

Jones MR, Rogers TJ, Worden K, Cross EJ, A bayesian methodology for localising acoustic emission sources in complex structures. Mech Syst Sig Process 163:108143. ISSN 0888- 3270., https:// /article/pii/S0888327021005239, 2022.

Oscar Serradilla, Ekhi Zugasti, Jon Rodriguez, · Urko Zurutuza, Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects, Applied Intelligence 52:10934–10964, 2021.

Arion Mircea, Monitorizarea şi diagnoza maşinii de inducţie prin analiza vibraţiilor, Teza de doctorat, UTCN, Cluj Napoca, Romania, 2014.

Revolutions Peter Poór, David Ženíšek, Josef Basl, Historical Overview of Maintenance Management Strategies: Development from Breakdown Maintenance to Predictive Maintenance in Accordance with Four Industrial, Proceedings of the International Conference on Industrial Engineering and Operations Management Pilsen, Czech Republic, ISSN / E-ISSN: 2169-8767, July 23-26, 2019.

Swedish Standard SS-EN 13306:2017, 2017.

Sezer, E.; Romero, D.; Guedea, F.; MacChi, M.; Emmanouilidis, C. An Industry 4.0-Enabled Low Cost Predictive Maintenance Approach for SMEs. In Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Stuttgart, Germany, ISSN: 1557-9662, 17–20 June; pp. 1–8, 2018.

Milena Nacchia, Fabio Fruggiero,, Alfredo Lambiase, Ken Bruton, A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector Appl. Sci., 11, 2546, ISSN 2076-3417, app11062546, 2021.

Reinforcement learning wiki/Reinforcement_learning,accesat 23.06.2023

Cho, S.; May, G.; Tourkogiorgis, I.; Perez, R.; Lazaro, O.; de la Maza, B. A hybrid machine learning approach for predictive maintenance in smart factories of the future. IFIP Adv. Inf. Commun. Technol, Springer International Publishing, ISSN 18684238, 536, pp. 311–317, 2018.

ISO 2372, Mechanical vibration of machines with operating speeds from 10 to 200 rev/s — Basis for specifying evaluation standards, 1974.

Risk mitigation strategy plan,, accesat 22.06.2023

Guidance for Performing Root Cause Analysis (RCA) with Performance Improvement Projects (PIPs),, accesat 24.06.2023.


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