Abstract
For many enterprises, engineering asset management (EAM) already had been an important part of daily management now. Especially in power supply chain. If there is a shutdown during operation, the stability and productivity will suffer a huge impact. It will directly affect the competitiveness of enterprises. Therefore, focusing on early prevention and instant diagnosis to maintain large transformers is the best important EAM of enterprises. However, with the significant increase of civil and industrial electricity consumption, the number of large transformers also increased. So, in order to solve the problem such as managing the big data generate by a large number of transformers. This paper integrates a Data Warehouse technology to establish fault diagnosis system. The system using transformer related data to establish different data cubes in Data Warehouse. These data cubes are applied for OLAP analysis in various decision support modules by using Multidimensional Expressions (MDX) code. Decision support modules including condition monitoring module, failure diagnostic module and intelligent diagnose module. Condition monitoring module will use the MDX code to provide multidimensional reports and graphical visualization of statistics. Further failure diagnostic module using three international dissolved gas analysis (DGA) methods, i.e., Institute of Electrical and Electronics Engineers (IEEE), International Electrotechnical Commission (IEC), and Electric Technology Research Association Japan (ETRAJ). Using MDX code’s statistic strength to integrate in these DGA methods’ diagnose step can help transformer maintainers find high potential faulty period. Finally, Intelligent diagnose module using MDX code to query multidimensional data cube to be the inputting parameters of Back Propagation Artificial Neural Network (BPANN) algorithm. Asset managers can diagnose potential transformer malfunctions and provide maintenance suggestions by using this system. The research methodology and system modules are evaluated and verified with real data from a series of 161 kV transformers in operations. This research focused on Data Warehouse technology application. The Data Warehouse technology is good at processing multidimensional data and can store great amount of data. It can help analyzers understand the data before making decision. The method used in this research can widely apply in many different fields, e.g., manufacturing, marketing and energy related fields.