A Regulated US Utility Prevents Critical Asset Failures with Predictive Intelligence

A regulated US utility serving electric and natural gas customers faced the familiar pressures of aging assets, resource constraints, severe weather, and a workforce in transition. Its infrastructure included critical substation equipment and transformers that had been operating for more than 40 years. Although scheduled inspections helped detect visible issues, the utility needed a better way to identify hidden conditions, connect siloed asset data and prevent critical failures before they disrupted service. 

Hitachi Energy deployed Asset Performance Management to aggregate asset data, assess risk and guide maintenance decisions. 

The solution used embedded asset performance models and predictive analytics to evaluate degrading conditions, calculate risk, and prioritize mitigation. It consolidated data from multiple silos, including nameplate, dissolved gas analysis, inspection, oil test, electrical test, and maintenance data, giving engineers a single source of actionable asset health insight. 

With a fleet-wide view of critical assets and connected lifecycle management workflows, the utility could prioritize high-risk transformers for immediate attention and reduce ineffective time-based practices in favor of risk-based maintenance. 

The solution turned disconnected data into maintenance intelligence and helped the utility act before asset risk became an outage. 

• Within the first 90 days, more than 2,000 transformers were entered into Asset Performance Management. 

• The system identified a transformer headed for failure, prompting action that saved lost revenue and helped customers avoid an unexpected outage. 

• Engineers gained a one-stop shop for asset information instead of navigating three to four disparate systems. 

• The utility reduced scheduled time-based tasks based on fleet-wide visibility of actual asset performance data. 

• The shift supported lower operating costs, enhanced work efficiencies, better service and more effective resource allocation.