Big Data: Data Mining for Asset Management

T&D organizations continue to face cost reduction pressures and at the same time they face increased pressure for high levels of service reliability. The latest name for the collective efforts intended to achieve this combined goal is "Asset Management". Asset Management can be defined as the process of maximizing asset profitability over the life of the asset. In the case of T&D and others utilities, we can be more specific and speak of "Equipment Asset Management".

T&D organizations have been through a recent round of cost cutting, and there is little remaining "low-hanging fruit" to help meet increasingly challenging goals. While further cost reductions may be possible, they can only be found through more detailed analysis of utility processes, costs, and equipment performance. This will require the effective use of data and "data mining" techniques. As with Asset Management, the term "data mining" invokes different ideas and images for different people. A very common use of data mining applies to consumer marketing and demographics data captured from sales transactions. In this discussion our focus is on the use of tools and techniques to provide business decision support information derived from operations and maintenance data sources.

The value of combining data mining with asset management is that the decision maker can more effectively manage the asset. This involves asking questions such as how much does the asset cost to own (i.e. operate, maintain, design) and what is its level of performance and "stress". What savings can be achieved by changing the design, operations / maintenance practices or configuration of the equipment or system? Should the equipment be replaced at some interval, and, if not, what strategy should be employed to prevent catastrophic failures of a population of similarly aged/stressed equipment? Data mining can also facilitate benchmarking, either the exchange of information or comparisons to prior performance.

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