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A Better Way To Maintain Lift

Predictive Maintenance: Save you time and money

Harness the Power of IoT and AI Technology

Modern high-speed lifts are way more sophisticated machines, the more complex lifts become the harder it is to maintain their availability and safety through traditional physical checks and fixed-schedule servicing routines.

When IoT and AI technologies come into play, they significantly ease the work of facility managers and building owners, especially for those who have to oversee lifts across multiple buildings.

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The  Benefits

Centralized interface for a diverse lift portfolio

As a non-invasive system, it allows to easily retrofit a fleet of lifts by various manufacturers, across multiple properties and connect them to a single interface. A unified dashboard provides a sweeping view over the current status of all lifts, making regular on-site inspections no longer necessary.

24/7 lift monitoring and real-time anomaly detection

The monitoring system continuously tracks key performance indicators of the lift and collects data on its condition, utilization, and ride comfort. The operation monitoring function provides an overview of all major operating parameters and physical characteristics of the lift and its components. Among other important parameters, it includes cabin floor leveling, number of trips, door movements, mileage, and environmental conditions. Criteria related to the passenger’s comfort, such as jerk and acceleration/deceleration, are also monitored. Operators can see current data and historical trends on the dashboard. This information is later used as a basis for predictive maintenance.
On top of that, the system promptly detects lift parameters that fall outside the predefined range for normal lift operations. Typical examples are mantrap, incorrect levelling of the cabin floor, door opening and closing issues, and abnormal vibration or noise. The monitoring system immediately alerts appropriate personnel when a major problem occurs, thus significantly improving critical incident response time.

Predictive maintenance to plan your lift repairs in advance

The predictive maintenance module of the monitoring system identifies and evaluates abnormal behavior or a change in operating parameters before the anomaly detection threshold is reached. It uses data interpretation and AI algorithms to predict and detect initial and ongoing degradation processes before an actual failure occurs. This complex and advanced function offers operators a major advantage as it allows them to proactively schedule service interventions for their lifts.
When a potentially abnormal process is detected, the monitoring system’s predictive maintenance module sends an alert to the dashboard. The alert indicates when a related lift component or subsystem is expected to fail. This information is displayed in a dedicated area of the dashboard as the Remaining Time to Downtime (RTD).
Further to this, the system identifies a root cause of a detected anomaly and provides clear guidance on what components need to be fixed. This enables better targeted maintenance planning, thereby saving costs on building operations and minimizing lift downtime. Lift technicians can be informed of the exact issue in advance and thus reduce their time on site by avoiding the need for a lengthy standard checklist to identify the root cause

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