{"id":30210,"date":"2024-08-29T11:15:03","date_gmt":"2024-08-29T11:15:03","guid":{"rendered":"https:\/\/www.metallicametals.com\/blog\/?p=30210"},"modified":"2026-09-16T07:31:23","modified_gmt":"2026-09-16T07:31:23","slug":"ai-tools-for-predictive-maintenance-in-utilities","status":"publish","type":"post","link":"https:\/\/www.metallicametals.com\/blog\/ai-tools-for-predictive-maintenance-in-utilities\/","title":{"rendered":"AI Tools for Predictive Maintenance in Utilities: The Pipeline"},"content":{"rendered":"

\"predictive<\/p>\n

By harnessing technology to anticipate and address equipment issues before they escalate, we\u2019re moving towards a more proactive approach. Third, pilot predictive maintenance on a defined group of assets. Connecting current inspection findings with prior maintenance and replacement records also helps utilities evaluate how similar components have performed under comparable conditions. EKN Engineering connects asset health, quality management, field inspection, and data infrastructure into workflows utilities can use across crews, contractors, regions, and programs. When predictive analytics is connected to maintenance planning, those insights can become prioritized inspections, targeted repairs, earlier replacement reviews, and better asset decisions. These patterns can help teams determine when comparable poles may warrant added inspection, treatment, reinforcement, or earlier replacement review rather than waiting for visible failure.<\/p>\n<\/p>\n