Emerson Introduces Liebert Battery Monitoring Service to Maximize Availability of Battery Systems
Emerson Network Power announced the availability of the Liebert Battery Monitoring Service. As the most robust monitoring and service solution of its kind, the offering is designed to maximize the availability and performance of battery systems.
The Liebert Battery Monitoring Service is meant to combine state-of-the-art battery monitoring technology with proactive maintenance and service response to deliver a complete battery solution that helps companies achieve high network availability, increase useful battery life and eliminate costly downtime. Whereas traditional battery monitoring programs are reactive by nature, rely on dial-up connections to deliver alarm data, and only notify end users after a problem has occurred, Emerson Network Power's customizable Liebert solution integrates on-site and remote preventive maintenance activities with predictive maintenance to identify problems before they occur.
"With Emerson's Liebert Battery Monitoring Service, data center managers now gain the confidence of knowing the true condition of their battery systems at all times and ensuring that their batteries are available when they need them," said Jeff Donato, service product manager at Emerson Network Power Service Business. "If a problem is developing, Emerson Network Power Customer Engineers will monitor and take corrective action before the trouble turns into a serious issue and possibly causes downtime. And, if a battery were to fail, customers will know quickly not only what the problem is, but also what Emerson Network Power is doing to fix it."
The Liebert Battery Monitoring Service relies on the latest Albér battery monitoring technology, and continuously diagnoses all critical battery parameters, such as cell voltage, overall string voltage, current and temperature. It provides a constant "pulse" on the system by gathering a steady stream of information about the batteries. Once data is gathered, it is archived and trended to help identify anomalies associated before they lead to failure.