-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
On August 15, it was learned that the first transformer voiceprint intelligent acquisition and identification equipment of Fujian Power Grid was recently put into operation
at the 110 kV Chengxiang substation in Putian.
The transformer voiceprint intelligent collection and recognition equipment is jointly developed by Fujian Electric Power Research Institute and Putian Power Supply Company, is a non-contact monitoring device, through the intelligent voiceprint collection device installed around the transformer, obtain the real-time operation status of the transformer voiceprint data, and then rely on the deep neural network algorithm to build fault characteristics voiceprint abnormal feature model, can analyze and judge the abnormal working conditions
of the equipment.
At present, the equipment has realized the intelligent analysis of four types of defects of the main transformer: partial discharge, DC bias, fan aging, and winding failure, and the accuracy of the monitoring results is 95.
3%.
On August 15, it was learned that the first transformer voiceprint intelligent acquisition and identification equipment of Fujian Power Grid was recently put into operation
at the 110 kV Chengxiang substation in Putian.
The transformer voiceprint intelligent collection and recognition equipment is jointly developed by Fujian Electric Power Research Institute and Putian Power Supply Company, is a non-contact monitoring device, through the intelligent voiceprint collection device installed around the transformer, obtain the real-time operation status of the transformer voiceprint data, and then rely on the deep neural network algorithm to build fault characteristics voiceprint abnormal feature model, can analyze and judge the abnormal working conditions
of the equipment.
At present, the equipment has realized the intelligent analysis of four types of defects of the main transformer: partial discharge, DC bias, fan aging, and winding failure, and the accuracy of the monitoring results is 95.
3%.