Condition Data Aggregation With Application to Failure Rate Calculation of Power Transformers

Cost-effective equipment maintenance for electric power transmission systems requires ongoing integration of information from multiple, highly distributed, and heterogeneous data sources storing various information about equipment. This paper describes a federated, query-centric data integration and knowledge acquisition framework for condition monitoring and failure rate prediction of power transformers. Specifically, the system uses substation equipment condition data collected from distributed data resources, some of which may be local to the substation, to develop Hidden Markov Models (HMMs) which transform the condition data into failure probabilities.