By using predictive maintenance data analytics and pre-emptively addressing potential issues, Socomec’s UPS ensures continuous operation, thereby reducing the risk of unexpected downtime and maintaining optimal performance for AI applications.

Spokesperson said:

The predictive maintenance algorithm closely monitors the state of health of key UPS components, specifically caps and fans, in real-time, based on the actual condition and usage of the UPS. By tracking various parameters and identifying any abnormal signals, the UPS machine learning can predict when components need to be replaced. This foresight allows for proper maintenance planning, determines exactly when it’s the best time to perform equipment maintenance and ensures that components are changed before they fail, avoiding any interruptions to the critical load.

Predictive maintenance not only extends the lifespan of the UPS equipment but also enhances its reliability. Moreover, this approach reduces downtime costs, improves the Total Cost of Ownership (TCO) by replacing components only when necessary, avoiding unnecessary maintenance costs and extending the life of the UPS.

Spokesperson said:

With the rise of Artificial Intelligence, existing concerns are compounded regarding heightened IT complexity, cybersecurity and the risks associated with an increased reliance on third parties, such as cloud or colocation providers. For example, as reliance on third parties has increased, the number of disruptive outages is trending upwards. Outages are prohibitively costly – whether due to human error, process and procedure or failings in security or systems. That’s why preventive and predictive maintenance strategies and approaches are so vital in terms of reducing that risk. It’s possible to reduce risk at every step – from the initial data centre design stage to the creation of IT architecture, infrastructure redundancy and training programmes as well as in terms of ongoing predictive maintenance

source= https://railway-news.com/the-power-of-predictive-maintenance/