# Smart Asset Management for Electric Utilities: Big Data and Future

**Authors:** Swasti R. Khuntia, Jose L. Rueda, Mart A. M. M. van der Meijden  
**arXiv:** [1706.09711](https://arxiv.org/abs/1706.09711)  
**Date:** 2017-06-18  
**Summary page:** https://www.summarizepaper.com/en/paper/1706.09711v2/

## Key points

- Electric utilities face future challenges in terms of big data and new technologies
- Large amounts of collected data are often underutilized due to volume and uncertainty
- Extracting valuable information from vast amounts of data is a question that needs addressing
- Big data analytics and machine learning techniques challenge the concept of "rich data and poor information"
- Advancements like IoT will enable smarter asset management practices for electric utilities
- Developing advanced and efficient algorithms for data analysis is crucial for addressing these challenges
- Effective maintenance depends on the quality, timeliness, accuracy, and completeness of information related to machine degradation state
- Figure 1 illustrates a supervised learning methodology for big data in electric utility asset management
- Transformation of unstructured large databases into structured ones using machine learning tools is essential
- The structured database can be used to develop a classification model for insights and predictions
- Leveraging big data analytics and new technologies is important for overcoming future challenges in electric utility asset management.

## Summary

This paper discusses the future challenges faced by electric utilities in terms of big data and new technologies. While utilities have been collecting large amounts of data, they are often underutilized due to their sheer volume and associated uncertainty. The condition monitoring of assets generates vast amounts of data during daily operations, raising the question of how to extract valuable information from this massive amount of data. The concept of "rich data and poor information" is being challenged by the emergence of big data analytics and machine learning techniques. With advancements like the Internet of Things (IoT), big data analytics will play a crucial role in enabling smarter asset management practices for electric utilities. To address these challenges, the paper emphasizes the need for developing more advanced and efficient algorithms for data analysis that can be readily accepted by utilities. Effective maintenance ultimately depends on the quality, timeliness, accuracy, and completeness of information related to machine degradation state, which serves as the basis for decision-making. Figure 1 illustrates a supervised learning methodology for big data in electric utility asset management. The unstructured large database is transformed into a structured large database using machine learning tools. This structured database is then used to develop a classification model that can provide insights and predictions. In conclusion, this paper highlights the importance of leveraging big data analytics and new technologies like IoT in order to overcome future challenges faced by electric utilities in managing their assets effectively. By following pathways and guidelines outlined in this study, current asset management practices can be made smarter to meet future needs.