An intelligent transformer maintenance system with real-time life assessment condition monitoring

Amy J.C. Trappey*, Charles V. Trappey, Yong Sun, Lin Ma, Leo Kuan Ju Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Transformers are critical assets that require continuous monitoring in the generation of electrical power. The management of engineering assets requires real-time diagnosis and preventive maintenance in order to avoid unexpected and catastrophic equipment shut-downs. In addition, the health status and remaining life of the transformer are critical knowledge for proper maintenance and management. This research focuses on oil-immersed transformers as a case study to help managers compile real-time monitoring and sampling data for information systems. The deterioration of the transformation's insulating paper was measured to derive the transformer's remaining life and to project the optimal replacement time. The proposed prediction method uses the Group Method of Data Handling (GMDH) to estimate the remaining life of any given transformer in use. This algorithm uses the dissolved gas-in-oil and furfural formation as the input variables to form the transformer life assessment model. Finally, the derived model is compared to models proposed by the International Electrotechnical Commission (IEC). The results show improved performance over the currently used IEC models.

Original languageEnglish
Pages (from-to)539-548
Number of pages10
JournalJournal of the Chinese Society of Mechanical Engineers, Transactions of the Chinese Institute of Engineers, Series C/Chung-Kuo Chi Hsueh Kung Ch'eng Hsuebo Pao
Issue number6
StatePublished - 1 Jan 2013


  • Engineering asset management
  • Failure diagnosis
  • Life assessment
  • Oil-immersed transformers


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