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Author (up) Carrasco, M.; Alvarez, F.; Velazquez, R.; Concha, J.; Perez-Cotapos, F. doi  openurl
  Title Brush-Holder Integrated Load Sensor Prototype for SAG Grinding Mill Motor Type
  Year 2019 Publication Electronics Abbreviated Journal Electronics  
  Volume 8 Issue 11 Pages 14 pp  
  Keywords electrical motor sensor; SAG grinding mill motor; inspection protocol; mining industry  
  Abstract One of the most widely used electro-mechanical systems in large-scale mining is the electric motor. This device is employed in practically every phase of production. For this reason, it needs to be inspected regularly to maintain maximum operability, thus avoiding unplanned stoppages. In order to identify potential faults, regular check-ups are performed to measure the internal parameters of the components, especially the brushes and brush-holders. Both components must be properly aligned and calibrated to avoid electric arcs to the internal insulation of the motor. Although there is an increasing effort to improve inspection tasks, most inspection procedures are manual, leading to unnecessary costs in inspection time, errors in data entry, and, in extreme cases, measurement errors. This research presents the design, development, and assessment of an integrated measurement prototype for measuring spring tension and other key parameters in brush-holders used in electric motors. It aims to provide the mining industry with a new, fully automatic inspection system that will facilitate maintenance and checking. Our development research was carried out specifically on the brush system of a SAG grinding mill motor. These machines commonly use SIEMENS motors; however, the instrument can be easily adapted to any motor by simply changing the physical dimensions of the prototype.  
  Address [Carrasco, Miguel; Concha, Javier; Perez-Cotapos, Francisco] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Av Diagonal Las Torres 20700, Santiago 7941169, Chile, Email: miguel.carrasco@uai.cl;  
  Corporate Author Thesis  
  Publisher Mdpi Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2079-9292 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000502269500023 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 1066  
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Author (up) Carrasco, M.; Mery, D.; Concha, A.; Velazquez, R.; De Fazio, R.; Visconti, P. doi  openurl
  Title An Efficient Point-Matching Method Based on Multiple Geometrical Hypotheses Type
  Year 2021 Publication Electronics Abbreviated Journal Electronics  
  Volume 10 Issue 3 Pages 246  
  Keywords computer vision; correspondence problem; fundamental matrix; multiple view geometry; point matching; trifocal tensor  
  Abstract Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2079-9292 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000614991900001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1341  
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