Daniela Borissova

Prof. Dr. Daniela Borissova

Information Processes and Decision Support Systems, INSTITUTE OF INFORMATION AND COMMUNICATION TECHNOLOGIES - BAS, Sofia, Sofia-Grad, Bulgaria
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Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2010 in Renewable Energy
Verified
Journal Article
Closed access
IMIvan Mustakerov
Ivan Mustakerov
Daniela BorissovaDaniela Borissova
Daniela Borissova


30 January 2015 in Trends Journal of Sciences Research
Verified
Journal Article
Open access
PDF file
Daniela BorissovaDaniela Borissova
Daniela Borissova
IMIvan Mustakerov
Ivan Mustakerov

20 December 2022
Verified
Book Chapter
Closed access
PDF file
Ivan GarvanovIvan Garvanov
Ivan Garvanov
Magdalena GarvanovaMagdalena Garvanova
Magdalena Garvanova
VIVladimir Ivanov
Vladimir Ivanov
ALAndon Lazarov
Andon Lazarov
Daniela BorissovaDaniela Borissova
Daniela Borissova
TKTodor Kostadinov
Todor Kostadinov


1 June 2012 in International Journal of Electronics and Telecommunications
Verified
Journal Article
Open access
PDF file
Daniela BorissovaDaniela Borissova
Daniela Borissova
IMIvan Mustakerov
Ivan Mustakerov
Lyubka DoukovskaLyubka Doukovska
Lyubka Doukovska
Predictive Maintenance Sensors Placement by Combinatorial Optimization: The strategy of predictive maintenance monitoring is important for successful system damage detection. Maintenance monitoring utilizes dynamic response information to identify the possibility of damage. The basic factors of faults detection analysis are related to properties of the structure under inspection, collect the signals and appropriate signals processing. In vibration control, structures response sensing is limited by the number of sensors or the number of input channels of the data acquisition system. An essential problem in predictive maintenance monitoring is the optimal sensor placement. The paper addresses that problem by using mixed integer linear programming tasks solving. The proposed optimal sensors location approach is based on the difference between sensor information if sensor is present and information calculated by linear interpolation if sensor is not present. The tasks results define the optimal sensors locations for a given number of sensors. The results of chosen sensors locations give as close as possible repeating the curve of structure dynamic response function. The proposed approach is implemented in an algorithm for predictive maintenance and the numerical results indicate that together with intelligent signal processing it could be suitable for practical application.