. "Hammer, Milo\u0161" . "LVQ neuronov\u00E1 s\u00ED\u0165 v prognostick\u00E9 klasifikaci zbytkov\u00E9 \u017Eivotnosti izola\u010Dn\u00EDho materi\u00E1lu vinut\u00ED elektrick\u00FDch stroj\u016F to\u010Div\u00FDch." . . "A question of the lifetime and reliability of materials of electrical rotary machines is still very important in present, because insulation material belongs to the most sensitive and costliest part of electrical machines. Hence there are developed new diagnostics methods, which evaluate the state of insulating system of electrical machine in process. This contribution deals with LVQ neural network usage in prognostic classification of the residual lifetime state of insulation material Relanex, which iss used for insulation of electrical rotary machine windings. The residual lifetime of electrical machine is verbally classified by virtue of measured input data. Classification by the help of neural network accordingly means the determination of the residual lifetime state to the predetermined number of categories which characterized a total state of insulating system electric rotary machine winding."@en . . "Sborn\u00EDk 23. Mezin\u00E1rodn\u00ED konference DIAGO (Technick\u00E1 diagnostika stroj\u016F a v\u00FDrobn\u00EDch za\u0159\u00EDzen\u00ED)" . . "insulating materials, Relanex, neuronal network"@en . . "2004-02-03+01:00"^^ . "LVQ Neural Network as a Tool for Classification of Residual Lifetime of High Voltage Insulation Material Relanex"@en . "Ot\u00E1zka \u017Eivotnosti a spolehlivosti izola\u010Dn\u00EDch materi\u00E1l\u016F elektrick\u00FDch stroj\u016F to\u010Div\u00FDch je v dne\u0161n\u00ED dob\u011B st\u00E1le velmi d\u016Fle\u017Eit\u00E1, nebo\u0165 izola\u010Dn\u00ED materi\u00E1l, u elektrick\u00FDm stroj\u016F pak izolace vinut\u00ED stroje pat\u0159\u00ED k nejcitliv\u011Bj\u0161\u00ED a nejn\u00E1kladn\u011Bj\u0161\u00ED \u010D\u00E1sti elektrick\u00E9ho za\u0159\u00EDzen\u00ED. Z tohoto d\u016Fvodu se vyv\u00EDjej\u00ED nov\u00E9 a zdokonaluj\u00ED ji\u017E zn\u00E1m\u00E9 diagnostick\u00E9 metody, kter\u00E9 zhodnocuj\u00ED stav izola\u010Dn\u00EDho syst\u00E9mu stroje v provozn\u00EDch podm\u00EDnk\u00E1ch. Nejprogresivn\u011Bj\u0161\u00EDmi z diagnostick\u00FDch metod jsou pak ty, kter\u00E9 nejen zhodnocuj\u00ED sou\u010Dasn\u00FD stav iizolace, ale sou\u010Dasn\u011B umo\u017E\u0148uj\u00ED ur\u010Ditou progn\u00F3zu jej\u00ED \u017Eivotnosti v dan\u00FDch provozn\u00EDch podm\u00EDnk\u00E1ch. \u010Cl\u00E1nek se zab\u00FDv\u00E1 vyu\u017Eit\u00EDm neuronov\u00E9 s\u00EDt\u011B LVQ v prognostick\u00E9 klasifikaci stavu zbytkov\u00E9 \u017Eivotnosti izola\u010Dn\u00EDho materi\u00E1lu Relanex, kter\u00FD se pou\u017E\u00EDv\u00E1 pro izolaci vinut\u00ED elektrick\u00FDch stroj\u016F to\u010Div\u00FDch. Zbytkov\u00E1 \u017Eivotnost elektrick\u00E9ho stroje je slovn\u011B klasifikov\u00E1na na z\u00E1klad\u011B name\u0159en\u00FDch vstupn\u00EDch dat. Klasifikace neuronovou s\u00EDt\u00ED tedy znamen\u00E1 ur\u010Den\u00ED stavu zbytkov\u00E9 \u017Eivotnosti do p\u0159edem zvolen\u00E9ho po\u010Dtu t\u0159\u00EDd charakterizuj" . . "P(GA102/03/0621)" . . "LVQ neuronov\u00E1 s\u00ED\u0165 v prognostick\u00E9 klasifikaci zbytkov\u00E9 \u017Eivotnosti izola\u010Dn\u00EDho materi\u00E1lu vinut\u00ED elektrick\u00FDch stroj\u016F to\u010Div\u00FDch." . "134-140" . . . "7"^^ . "RIV/00216305:26210/04:PU48315" . "1"^^ . . . "LVQ neuronov\u00E1 s\u00ED\u0165 v prognostick\u00E9 klasifikaci zbytkov\u00E9 \u017Eivotnosti izola\u010Dn\u00EDho materi\u00E1lu vinut\u00ED elektrick\u00FDch stroj\u016F to\u010Div\u00FDch."@cs . "571917" . "LVQ neuronov\u00E1 s\u00ED\u0165 v prognostick\u00E9 klasifikaci zbytkov\u00E9 \u017Eivotnosti izola\u010Dn\u00EDho materi\u00E1lu vinut\u00ED elektrick\u00FDch stroj\u016F to\u010Div\u00FDch."@cs . . . . "LVQ Neural Network as a Tool for Classification of Residual Lifetime of High Voltage Insulation Material Relanex"@en . "1"^^ . "[C76DD254779F]" . "26210" . . . "HART PRESS, sro. Otrokovice" . "80-248-0465-4" . "RIV/00216305:26210/04:PU48315!RIV06-GA0-26210___" . "Ostrava" . "Ostrava" . "Ot\u00E1zka \u017Eivotnosti a spolehlivosti izola\u010Dn\u00EDch materi\u00E1l\u016F elektrick\u00FDch stroj\u016F to\u010Div\u00FDch je v dne\u0161n\u00ED dob\u011B st\u00E1le velmi d\u016Fle\u017Eit\u00E1, nebo\u0165 izola\u010Dn\u00ED materi\u00E1l, u elektrick\u00FDm stroj\u016F pak izolace vinut\u00ED stroje pat\u0159\u00ED k nejcitliv\u011Bj\u0161\u00ED a nejn\u00E1kladn\u011Bj\u0161\u00ED \u010D\u00E1sti elektrick\u00E9ho za\u0159\u00EDzen\u00ED. Z tohoto d\u016Fvodu se vyv\u00EDjej\u00ED nov\u00E9 a zdokonaluj\u00ED ji\u017E zn\u00E1m\u00E9 diagnostick\u00E9 metody, kter\u00E9 zhodnocuj\u00ED stav izola\u010Dn\u00EDho syst\u00E9mu stroje v provozn\u00EDch podm\u00EDnk\u00E1ch. Nejprogresivn\u011Bj\u0161\u00EDmi z diagnostick\u00FDch metod jsou pak ty, kter\u00E9 nejen zhodnocuj\u00ED sou\u010Dasn\u00FD stav iizolace, ale sou\u010Dasn\u011B umo\u017E\u0148uj\u00ED ur\u010Ditou progn\u00F3zu jej\u00ED \u017Eivotnosti v dan\u00FDch provozn\u00EDch podm\u00EDnk\u00E1ch. \u010Cl\u00E1nek se zab\u00FDv\u00E1 vyu\u017Eit\u00EDm neuronov\u00E9 s\u00EDt\u011B LVQ v prognostick\u00E9 klasifikaci stavu zbytkov\u00E9 \u017Eivotnosti izola\u010Dn\u00EDho materi\u00E1lu Relanex, kter\u00FD se pou\u017E\u00EDv\u00E1 pro izolaci vinut\u00ED elektrick\u00FDch stroj\u016F to\u010Div\u00FDch. Zbytkov\u00E1 \u017Eivotnost elektrick\u00E9ho stroje je slovn\u011B klasifikov\u00E1na na z\u00E1klad\u011B name\u0159en\u00FDch vstupn\u00EDch dat. Klasifikace neuronovou s\u00EDt\u00ED tedy znamen\u00E1 ur\u010Den\u00ED stavu zbytkov\u00E9 \u017Eivotnosti do p\u0159edem zvolen\u00E9ho po\u010Dtu t\u0159\u00EDd charakterizuj"@cs .