Efficient inspection planning, supported through diagnoses, forecasts, and maintenance suggestions
“Guardione® has a great advantage of predicting asset conditions with big data-based AI diagnosis, thus we were able to prepare for the asset replacements at optimal times. We use guardione® solution for the management of our transformer’s conditions and we trust it to prevent unexpected failures”
[Account’s Maintenance Team Engineer]
Account Overview
This account is a power generation company whose main business is the collective energy business of supplying heat and energy within industrial complexes through cogeneration as well as overseas solar power generation.
Challenge
There were several sub-transformers in the client's cogeneration plant, which had been continuously exposed to high loads for nearly ten years. Because the overhaul and inspection plan sought by the client could not be established with simple diagnosis and the existing insulating oil analysis, the transformers were being operated despite the worry and anxiety.
The transformers had not been inspected or shut down for a long time, so a full-scale detailed inspection had to be done at some point.
Project
Onepredict has been officially recognized for its predictive diagnosis technology and algorithm excellence by participating as a joint developer with Korea Electric Power Corporation.
The solution was thus taken into consideration for its ability to offer comprehensive inspection insights. The client sought to gain insight into the status of the transformer, which the client’s company had been keeping a close watch on, through accurate diagnosis and maintenance suggestions.
Result
Guardione® solution successfully conducted a verification process, offering the outcome the client was looking for.
1. Using its industrial AI algorithm, guardione® solution identified transformers that would require special maintenance within the next 5 years.
2. The client selected only transformers as detailed inspection targets that would potentially require special maintenance, saving on unnecessary detailed inspection expenses and increasing operation efficiency.
Efficient inspection planning, supported through diagnoses, forecasts, and maintenance suggestions
“Guardione® has a great advantage of predicting asset conditions with big data-based AI diagnosis, thus we were able to prepare for the asset replacements at optimal times. We use guardione® solution for the management of our transformer’s conditions and we trust it to prevent unexpected failures”
[Account’s Maintenance Team Engineer]
Account Overview
This account is a power generation company whose main business is the collective energy business of supplying heat and energy within industrial complexes through cogeneration as well as overseas solar power generation.
Challenge
There were several sub-transformers in the client's cogeneration plant, which had been continuously exposed to high loads for nearly ten years. Because the overhaul and inspection plan sought by the client could not be established with simple diagnosis and the existing insulating oil analysis, the transformers were being operated despite the worry and anxiety.
The transformers had not been inspected or shut down for a long time, so a full-scale detailed inspection had to be done at some point.
Project
Onepredict has been officially recognized for its predictive diagnosis technology and algorithm excellence by participating as a joint developer with Korea Electric Power Corporation.
The solution was thus taken into consideration for its ability to offer comprehensive inspection insights. The client sought to gain insight into the status of the transformer, which the client’s company had been keeping a close watch on, through accurate diagnosis and maintenance suggestions.
Result
Guardione® solution successfully conducted a verification process, offering the outcome the client was looking for.
1. Using its industrial AI algorithm, guardione® solution identified transformers that would require special maintenance within the next 5 years.
2. The client selected only transformers as detailed inspection targets that would potentially require special maintenance, saving on unnecessary detailed inspection expenses and increasing operation efficiency.