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OUR PLATFORM INTEGRATED SOLUTION
How does the solution work?
Identify and rectify anomalies, minimize time spent in equipment maintenance, minimize production hours, minimize costs. Optimize key fixed assets and machinery by collecting critical data.
Furthermore, predicting potential anomalies, and enabling your workforce to manage assets with maximized efficiency. Collect key machine data via vibration sensors, easily add predictive analytics with our NoCode machine learning to assess future risks.
Given Value
This is how Predictive Maintenance maximizes your business outcome
Reduction of downtime
Reduced maintenance costs
Increased
asset lifetime
Improved
workplace safety
Increased ROI
EXAMPLE CASE
How can you use predictive maintenance for your company?
KEY BENEFIT
"Maintenance is only performed on machines when it is required - before failure is likely to occur bringing several cost savings."
An elevator company wants to make sure that its elevators work efficiently at all times. If the elevators would stop working in case of failure, this would cause many inconveniences for customers of shopping malls, especially seniors.
Example case
Let's co-innovate together.
Targeted outcomes with our
predictive maintenance solution
Reduced downtime
Maintenance workers get warned weeks before problems with elevators arise due to malfunctioning components, allowing for up to 12 hours of unexpected downtime to be avoided.
1
Machine analysis
Analysis of machine condition monitoring data against previous data could be implied, which would offer alarms, diagnostics, and forecasts to maintenance engineers before functional breakdown occurs.
2
Effective monitoring
Tracking of critical machinery as well as stability of elevators across all areas and related infrastructure, with more assets frequently and easily to be added.
3
Reduced potential harm
Maintenance of machines implied depending on their condition rather than a strict schedule which would allow them to act only when absolutely necessary, lowering the risk of harm to people or machines.
