FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387010762> ?p ?o ?g. }

• W4387010762 endingPage "12" @default.
• W4387010762 startingPage "1" @default.
• W4387010762 abstract "The recent development of highly automated machinery and intelligent industrial plants has increasingly enabled the continuous monitoring of their efficiency and condition, with the aim of maintaining high production efficiency and minimal malfunctions. Typical condition monitoring and fault detection applications are often achieved using acoustic and vibrational techniques, but the availability of distributed electrical measurements opens new opportunities for industrial fault detection with minimal impact on electrical systems. Even if artificial intelligence-based approaches can be used to model industrial equipment by means of measures made on electrical systems to which they are connected, machine learning algorithms have been demonstrated to be particularly adequate for this purpose due to the huge amount of data produced by interconnected sensors and devices. In this context, the aim of this work is to propose a new unsupervised analysis methodology for detecting anomalies in industrial machinery using electrical current values and other parameters measured on the power grid. The proposed framework is aimed at incorporating the advantages of Machine Learning algorithms and those of traditional analysis, optimizing their operation to improve performance and execution time; this also incorporates a methodology for analyzing the temporal dynamics of the anomaly based on Short-time Fourier transform to strengthen the performance of the detection. The results obtained showed excellent performance, both compared to the evaluations of a technical expert and to other methodologies used in the literature, with zero false positives detected in all datasets tested and a negligible number of undetected outlier events, less than 4% of the total in the datasets." @default.
• W4387010762 created "2023-09-26" @default.
• W4387010762 creator A5014275270 @default.
• W4387010762 creator A5016248242 @default.
• W4387010762 creator A5021219850 @default.
• W4387010762 creator A5029223273 @default.
• W4387010762 creator A5034503288 @default.
• W4387010762 creator A5052137039 @default.
• W4387010762 creator A5061329809 @default.
• W4387010762 creator A5083694119 @default.
• W4387010762 date "2023-01-01" @default.
• W4387010762 modified "2023-10-12" @default.
• W4387010762 title "A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems" @default.
• W4387010762 cites W1178595422 @default.
• W4387010762 cites W1409440556 @default.
• W4387010762 cites W1537039776 @default.
• W4387010762 cites W2122646361 @default.
• W4387010762 cites W2149621757 @default.
• W4387010762 cites W2810463702 @default.
• W4387010762 cites W2901274259 @default.
• W4387010762 cites W2934533499 @default.
• W4387010762 cites W2941584439 @default.
• W4387010762 cites W2942231644 @default.
• W4387010762 cites W2946159658 @default.
• W4387010762 cites W2949488362 @default.
• W4387010762 cites W3048804154 @default.
• W4387010762 cites W3085771300 @default.
• W4387010762 cites W3092011861 @default.
• W4387010762 cites W3101981467 @default.
• W4387010762 cites W3109842669 @default.
• W4387010762 cites W3114000978 @default.
• W4387010762 cites W3131541362 @default.
• W4387010762 cites W3135549020 @default.
• W4387010762 cites W3135550350 @default.
• W4387010762 cites W3137091086 @default.
• W4387010762 cites W3155739706 @default.
• W4387010762 cites W3170851865 @default.
• W4387010762 cites W3196584685 @default.
• W4387010762 cites W3207988956 @default.
• W4387010762 cites W3210736123 @default.
• W4387010762 cites W3212355623 @default.
• W4387010762 cites W4200101241 @default.
• W4387010762 cites W4206634698 @default.
• W4387010762 cites W4211206835 @default.
• W4387010762 cites W4214614245 @default.
• W4387010762 cites W4225080122 @default.
• W4387010762 cites W4281784047 @default.
• W4387010762 cites W4282972329 @default.
• W4387010762 cites W4283262732 @default.
• W4387010762 cites W4283750640 @default.
• W4387010762 cites W4283763162 @default.
• W4387010762 cites W4288039037 @default.
• W4387010762 cites W4289792655 @default.
• W4387010762 cites W4289931049 @default.
• W4387010762 cites W4291366336 @default.
• W4387010762 cites W4297478646 @default.
• W4387010762 cites W4307980314 @default.
• W4387010762 cites W4317600356 @default.
• W4387010762 cites W4363650848 @default.
• W4387010762 cites W4366139813 @default.
• W4387010762 cites W4384703568 @default.
• W4387010762 doi "https://doi.org/10.1109/tim.2023.3318684" @default.
• W4387010762 hasPublicationYear "2023" @default.
• W4387010762 type Work @default.
• W4387010762 citedByCount "0" @default.
• W4387010762 crossrefType "journal-article" @default.
• W4387010762 hasAuthorship W4387010762A5014275270 @default.
• W4387010762 hasAuthorship W4387010762A5016248242 @default.
• W4387010762 hasAuthorship W4387010762A5021219850 @default.
• W4387010762 hasAuthorship W4387010762A5029223273 @default.
• W4387010762 hasAuthorship W4387010762A5034503288 @default.
• W4387010762 hasAuthorship W4387010762A5052137039 @default.
• W4387010762 hasAuthorship W4387010762A5061329809 @default.
• W4387010762 hasAuthorship W4387010762A5083694119 @default.
• W4387010762 hasBestOaLocation W43870107621 @default.
• W4387010762 hasConcept C119599485 @default.
• W4387010762 hasConcept C119857082 @default.
• W4387010762 hasConcept C124101348 @default.
• W4387010762 hasConcept C127313418 @default.
• W4387010762 hasConcept C127413603 @default.
• W4387010762 hasConcept C151730666 @default.
• W4387010762 hasConcept C152745839 @default.
• W4387010762 hasConcept C154945302 @default.
• W4387010762 hasConcept C165205528 @default.
• W4387010762 hasConcept C172707124 @default.
• W4387010762 hasConcept C175551986 @default.
• W4387010762 hasConcept C200601418 @default.
• W4387010762 hasConcept C2775846686 @default.
• W4387010762 hasConcept C2779343474 @default.
• W4387010762 hasConcept C41008148 @default.
• W4387010762 hasConcept C64869954 @default.
• W4387010762 hasConcept C739882 @default.
• W4387010762 hasConcept C79337645 @default.
• W4387010762 hasConcept C8038995 @default.
• W4387010762 hasConcept C86803240 @default.
• W4387010762 hasConceptScore W4387010762C119599485 @default.
• W4387010762 hasConceptScore W4387010762C119857082 @default.
• W4387010762 hasConceptScore W4387010762C124101348 @default.

• next