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28/01/2026

Why Predictive Maintenance Starts with Better DGA Interpretation

42% of power transformer failures occur inside the main tank and are detectable by dissolved gas analysis (DGA), making DGA the number one diagnostic tool for at-risk transformers. However, conventional interpretation methods focus on gas thresholds rather than true failure risk. Many transformers fail before reaching these limits, while others operate for years with elevated gases. Instead, statistical reliability modeling offers a more scientific, data-driven framework for failure prediction. Based on a study of over 15,000 operational units and 300 failure cases, we compare traditional DGA interpretation to reliability-based approaches that better inform maintenance decisions, reduce uncertainty, and improve asset management. 

 

Discussion Questions:  

What makes conventional DGA interpretation unreliable in certain real-world cases? 

How might Reliability-based DGA change asset replacement or monitoring strategies? 

What role does statistical failure data play in prioritizing transformers for maintenance or replacement? 

 

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