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1. WO2020005541 - MACHINE LEARNING ANALYSIS OF PIPING AND INSTRUMENTATION DIAGRAMS

Publication Number WO/2020/005541
Publication Date 02.01.2020
International Application No. PCT/US2019/036651
International Filing Date 12.06.2019
IPC
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
19
Programme-control systems
02
electric
418
Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control (DNC), flexible manufacturing systems (FMS), integrated manufacturing systems (IMS), computer integrated manufacturing (CIM)
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13
Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02
electric
04
involving the use of models or simulators
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
G
ANALOGUE COMPUTERS
7
Devices in which the computing operation is performed by varying electric or magnetic quantities
48
Analogue computers for specific processes, systems, or devices, e.g. simulators
66
for control systems
G PHYSICS
05
CONTROLLING; REGULATING
B
CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
19
Programme-control systems
02
electric
04
Programme control other than numerical control, i.e. in sequence controllers or logic controllers
05
Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
G05B 19/418 (2006.01)
G05B 13/04 (2006.01)
G06G 7/66 (2006.01)
G05B 19/05 (2006.01)
CPC
G06K 2209/01
G06K 9/00442
G06K 9/00476
G06K 9/4628
G06K 9/6256
G06K 9/6262
Applicants
  • SCHNEIDER ELECTRIC SYSTEMS USA, INC. [US/US]; 38 Neponset Avenue C42-12 Foxboro, MA 02035, US
Inventors
  • SINHA, Bhaskar; IN
  • PATIL, Ashish; IN
  • BHATTACHARYYA, Amitabha; IN
  • JAGANNATH, Venkatesh; US
  • KONDEJKAR, Sameer; IN
Agents
  • BAIN, Robert, M.; US
  • JAMES, Kurt, F.; US
  • BOETTLER, Jeannie, M.; US
  • CARLSON, Judith, L.; US
  • CONWAY, Jason, H.; US
  • CRONIN, James, J.; US
  • EIDSON, Bradley, S.; US
  • EMNETT, Christine, M.; US
  • EVANS, JR., Robert, M.; US
  • EVERDING, William, R.; US
  • FLEISCHUT, Paul, I.; US
  • GODAR, Michael, E.; US
  • GRAY, Colleen, N.; US
  • HARTLEY, Michael, J.; US
  • HENDRICKSON, Janet, S.; US
  • KEIL, Vincent, M.; US
  • JOHNSON, Morgan, L.; US
  • JORDAN, Jamaal, R.; US
  • KAZMIERSKI, Steven, T.; US
  • KIM, David, S.; US
  • LEVITT, Steven, N.; US
  • MEHTA, Samir, R.; US
  • MILLARD, Elizabeth, E.; US
  • MARKOWSKI PETRILLO, Kathleen, M.; US
  • POLLMANN, Jonathan, G.; US
  • SCHROEDER, John, R.; US
  • SELLERS, Andrea, F.; US
  • SLICER, Penny, R.; US
  • TASSI, Elizabeth, A.; US
  • TIETZ, Paul, D.; US
  • TURNER, Colin, W.; US
  • WEGMAN, Andrew, C.; US
Priority Data
16/021,86728.06.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) MACHINE LEARNING ANALYSIS OF PIPING AND INSTRUMENTATION DIAGRAMS
(FR) ANALYSE D'APPRENTISSAGE MACHINE DE DIAGRAMMES DE TUYAUTERIE ET D'INSTRUMENTATION
Abstract
(EN)
Automated evaluation and extraction of information from piping and instrumentation diagrams (P&IDs). Aspects of the systems and methods utilize machine learning and image processing techniques to extract relevant information, such as tag names, tag numbers, and symbols, and their positions, from P&IDs. Further aspects feed errors back to a machine learning system to update its learning and improve operation of the systems and methods.
(FR)
L'évaluation et l'extraction automatisées d'informations à partir de diagrammes de tuyauterie et d'instrumentation (P&ID) sont automatisées. Certains aspects des systèmes et des procédés font appel à des techniques d'apprentissage machine et de traitement d'image pour extraire des informations pertinentes, telles que des noms d'étiquette, des numéros d'étiquette et des symboles, ainsi que leurs positions, de P&ID. D'autres aspects fournissent des erreurs de rétroaction à un système d'apprentissage machine pour en mettre à jour l'apprentissage et perfectionner le fonctionnement des systèmes et des procédés.
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