FIELD: measurement technology.
SUBSTANCE: invention relates to diagnosis of technical electromechanical equipment. Method comprises measuring parameters, calculating signs of rapid diagnosis, identifying faults and calculating deviations, taking into account algorithms of artificial neural network of Kohonen type, based on which are calculated and estimated coefficients of technical state of consumer-regulator by diagnostic subsystems, then based on obtained values of coefficients, technical condition is evaluated and diagnostic mode is selected, wherein coefficients are calculated taking into account changes in electric and vibration parameters over observed period of operation, then deviation of registered parameters is compared against standardized ones using neural network of Kohonen type and possibility of operation of electric installation as consumer-regulator is determined.
EFFECT: high accuracy and quality of assessing the technical state of equipment.
1 cl, 3 dwg, 3 tbl
Title | Year | Author | Number |
---|---|---|---|
METHOD OF THE MODES CONTROL BASED ON THE NEURAL NETWORK DIAGNOSTICS OF FAULTS AND TECHNICAL CONDITION OF THE ELECTRIC-DRIVE GAS-COMPRESSOR UNIT | 2017 |
|
RU2648413C1 |
METHOD FOR DIAGNOSING INFORMATION-CONVERTING ELEMENTS OF AIRCRAFT ON-BOARD EQUIPMENT BASED ON MACHINE LEARNING | 2022 |
|
RU2802976C1 |
METHOD FOR DIAGNOSING A COMPLEX OF ON-BOARD EQUIPMENT OF AIRCRAFT BASED ON MACHINE LEARNING AND A DEVICE FOR ITS IMPLEMENTATION | 2023 |
|
RU2816667C1 |
DEVICE FOR TECHNICAL DIAGNOSTICS OF AIRCRAFT ON-BOARD EQUIPMENT COMPLEX BASED ON MACHINE LEARNING | 2024 |
|
RU2831917C1 |
METHOD FOR DIAGNOSING A COMPLEX OF ON-BOARD EQUIPMENT OF AIRCRAFT BASED ON UNSUPERVISED MACHINE LEARNING WITH AUTOMATIC DETERMINATION OF MODEL TRAINING PARAMETERS | 2023 |
|
RU2818858C1 |
METHOD FOR DIAGNOSING AIRCRAFT ON-BOARD EQUIPMENT COMPLEX BASED ON MACHINE LEARNING | 2023 |
|
RU2809719C1 |
DIAGNOSTIC METHOD OF TECHNICAL STATE OF GAS-TURBINE ENGINE | 2010 |
|
RU2445598C1 |
METHOD FOR DIAGNOSIS OF COMPLEX TECHNICAL OBJECTS | 2014 |
|
RU2582876C2 |
METHOD FOR DETERMINATION OF TECHNICAL CONDITION OF ELECTRIC AND HYDRAULIC DRIVES | 2022 |
|
RU2799489C1 |
METHOD FOR DIAGNOSTICS OF ASYNCHRONOUS ELECTRIC MOTOR BASED ON NEURAL NETWORK ANALYSIS | 2024 |
|
RU2831697C1 |
Authors
Dates
2020-04-20—Published
2019-12-16—Filed