Smart Predictive Maintenance Device for Critical In-Service Motors
Emil Cazacu,
Lucian-Gabriel Petrescu and
Valentin Ioniță
Additional contact information
Emil Cazacu: Department of Electrical Engineering, Faculty of Electrical Engineering, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
Lucian-Gabriel Petrescu: Department of Electrical Engineering, Faculty of Electrical Engineering, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
Valentin Ioniță: Department of Electrical Engineering, Faculty of Electrical Engineering, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
Energies, 2022, vol. 15, issue 12, 1-16
Abstract:
The paper proposed an innovative predictive maintenance system, designated to monitor and diagnose critical electrical equipment (generally large power electric motors) within industrial electrical installations. A smart and minimally invasive system is designed and developed. Its scope is to evaluate continuously the essential operating parameters (electrical, thermal, and mechanical) of the investigated equipment. It manages to report the deviations of inspected machine operating parameters values from the rated ones. The system also suggests the potential cause of these abnormal variations along with possible means (if the defect is identified in a database, constantly updated with each appearance of a malfunction). The developed maintenance device generates an operating report of the analyzed equipment, in which the values of power quality and energy indicators are computed and interpreted. Additionally, real-time remote transmission of analyzed data is facilitated, making them accessible from any location. The proposed maintenance system is a low-cost device that is easy to install and use in comparison with similar existing devices and equipment. The designed maintenance system was tested on dedicated to low-voltage equipment up to 100 kW.
Keywords: electric critical equipment; induction motors; predictive maintenance; power quality; experimental device (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://www.mdpi.com/1996-1073/15/12/4283/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/12/4283/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:12:p:4283-:d:836384
Access Statistics for this article
Energies is currently edited by Ms. Agatha Cao
More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().