Lower costs
Lower costs over the equipment’s lifetime with a tailored maintenance program that keeps components in good condition and recommends parts replacement based on their actual condition and usage.
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Predictive maintenance uses real-time monitoring and data analytics to anticipate potential problems and schedule well-timed actions. It combines remote diagnostics with onsite work by technicians to ensure that elevators operate reliably and safely. Compared to conventional reactive maintenance, predictive maintenance improves service outcomes, reduces costs and provides better reporting.
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Traditional maintenance follows a fixed schedule, replacing parts based on age or wear. Predictive maintenance utilizes usage data and equipment data to spot issues early, to schedule the right actions, thus keeping elevators running longer with fewer disruptions and lower lifetime costs.
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Predictive maintenance reduces the likelihood and duration of downtime by addressing many issues before they affect service. In KONE’s connected elevator base, outcomes include resolving 70% of issues proactively and reducing downtime by 30%. Actual results vary by site, equipment, connectivity, and market availability. Not all faults can be prevented.
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Operations and performance data is collected from equipment controllers, inbuilt equipment sensors and from additional sensors for 3rd-party equipment and shared via a secured connection to the IoT cloud services. Innovative AI-based data analytics identifies patterns and early warning signs. The system generates alerts, recommends actions and updates the maintenance plan accordingly. Building managers can see real-time equipment status and service information in KONE’s digital tools. Availability of specific capabilities (for example, KONE Remote Rescue) may vary by equipment, contract and market.