Utilities depend on fixed assets spread widely across their service territories to keep the electricity, gas, and water flowing.  These assets need to be maintained to prevent service interruptions.  As we saw in California, the consequences of poor maintenance can be much more than just service interruption.  According to the New York Times, downed power lines caused by poor maintenance were partially responsible for some of the wildfires there last year.

Maintaining these assets is expensive.  Crews need to be dispatched and supplies need to be stocked.  Often the assets are in remote locations, making them even more expensive and difficult to maintain and putting a premium on getting the maintenance right the first time.  In addition, the data needed to maintain these assets often is maintained in many places, some not even machine readable.  It needs to be integrated to provide a complete view of the asset. 

This utility data often includes:

• An inventory of the assets, including their installation dates and vendors
• Records of on-site inspections
• Soil tests, particularly for power poles
• Vegetation
• Maintenance histories
• Weather data
• Load data (e.g. kilowatt hours, cubic feet, or gallons transported)

HEXstream’s principals helped an electric utility address this problem, and we can do the same for you. The impetus for the project was to allocate a budget for preventive maintenance at the beginning of the year based on the health of assets and their likelihood to fail during the year. This utility serves over 500,000 customers across over 18,000 sq. mi. (46,000 sq. km) of territory.  They needed to keep their assets in top shape, but they found their inspection and maintenance crews were spending too much time fixing small problems at the expense of larger problems. The result was a high enough number of outages that it was affecting their FERC metrics.

HEXstream’s principals worked with the utility to build a predictive maintenance system that integrated the data from over a dozen operational data silos like CIS, GTech, PI, WMS, NMS, ERP, P3, etc. mentioned above and several others – a typical scenario at almost all utility companies.  It also had a scoring algorithm to predict what assets would require maintenance.  The utility then could deploy its repair crews to fix the assets most critical to reliable network operation.  Within six months of going live, the utility saw a marked improvement in its FERC metrics.  They also found they could cut back on their fees to third-party surveyors because they could target them to inspect specific assets most likely to need repair and increase inspections on equipment likely to cause major damage if it failed.  They also found they could budget maintenance more accurately.