Transformer health indexing (THI) is an industrial strategy to rank transformers within a fleet according to their intervention priority. A THI score can help in ensuring commendable decisions on repair, replacement, and refurbishment of transformers in a grid. They can also determine the risk, reliability or both for each asset simultaneously. Nowadays, THI strategies are at the core of many “wholesome” but elaborate and expensive asset performance management (APM) suites. From the client’s perspective the only daunting question is, “how long do we have before this one goes out?”. An excellent discussion on the use of Ronin AI to ensure production uptime and optimal transformer performance for steel makers is also available.

With the advent of AI, Seetalabs’ has developed a tool to predict the THI score in real time using typical oil condition monitoring data from transformers. But the real question is, what is the best way to use Ronin AI to ensure optimal transformer performance?

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What is Ronin AI and how does it help the transformer industry?

What are the benefits of using Ronin AI?

Best practices for using Ronin AI for transformer fleet management

Ronin AI works best with CSV files containing at least the dissolved gases prescribed. The accuracy and precision moves multifold if all data is available. However, if some data is missing, Ronin AI uses the best recognition models to curate the most potential health index scenario and predicts the score. The confidence of such predictions are within a limit of 60-70% and can improve greatly if larger datasets, such as for fleets are uploaded, instead of a standalone transformer.

So don’t wait and grab your free trial right away!