Downtime is costly. Especially in today’s always-on world, where even the shortest outage can mean lost revenue, damaged trust, and missed SLAs. That’s why the pressure to keep backup systems resilient has never been greater. And yet, traditional maintenance practices like scheduled discharge tests leave much to be desired—they’re risky, expensive, and still leave gaps in coverage.
Traditional UPS maintenance is costly, risky, and inefficient
Service providers typically manage thousands of backup battery units, known as Uninterruptible Power Supply (UPS) systems. But keeping them truly “uninterruptible” is often a high-maintenance and high-cost operation.
To confirm whether these backup batteries are still fit for purpose, scheduled discharge tests are performed at regular intervals. If a battery passes the test—great, it stays in place until the next check. But if it shows signs of wear, operators are faced with a dilemma:
Do you leave a potentially degraded battery in service, risking failure when disaster strikes? Or do you replace it early, sacrificing value by retiring a unit that might still have life left? Besides, even batteries that passed the test with flying colors aren’t immune to issues cropping up between tests.
Increasing the frequency of tests isn’t the answer either. These tests cause stress on the UPS system, and each test introduces a temporary gap in backup coverage, adding operational risk and complexity.
The only way to solve this dilemma is to ensure that you get proactive insights on your UPS systems in between scheduled discharge tests. And that’s where AI comes in.
Cut costs and outages with proactive AI-driven UPS maintenance
By complementing periodic discharge testing with AI-driven monitoring, network operators can proactively identify issues as they emerge, reduce emergency repairs, and extend the life of their UPS investments.
The DataMiner Relational Anomaly Detection (RAD) feature uses AI and machine learning to analyze real-time battery data across your network. It immediately detects anomalies—like early signs of battery degradation—so operators can schedule targeted replacements before outages occur. At the same time, it helps you squeeze more life—and value—out of your UPS investment. This results in lower operational costs and greater uptime across the board.
Start cutting costs and outages today
Starting from DataMiner feature release 10.5.4 and main release 10.6.0, all customers can now access Relational Anomaly Detection. Just head to the DataMiner Catalog and download the RAD Manager to get started.
Or request a 15-minute demo today and see how DataMiner can help you prevent outages, cut costs, and protect your network’s power backbone.