Planned preventative maintenance for lifts

Planned preventative maintenance for lifts involves regularly scheduled visits by qualified engineers. Through planned regular visits, engineers will provide inspections, lubrication, adjustments and repairs generally according to a pre-planned maintenance schedule, aimed to ensure optimal performance, safety, longevity and no unplanned interventions.

Some maintenance schedules will identify specific tasks for each individual visit, which often differ between visits within the maintenance schedule, defining when certain components will be inspected. If the scheduled visit for a specific component is not completed as planned or is incorrectly scheduled it can lead to failures.

In the realm of vertical transportation, maintaining lifts effectively is crucial for both safety and operational efficiency. The primary approach has always been regular maintenance, using personnel to attend your property and provide hands on preventative maintenance. However, predictive maintenance and maintenance monitoring are now playing a significant role in convincing lift owners they can improve the reliability of their lifts. But the two approaches differ fundamentally in their methodologies and outcomes and cannot be conflated.

Maintenance monitoring: A reactive approach  

Maintenance monitoring refers to the continuous tracking of lift performance and status to identify faults or irregularities. Typically, lifts are equipped with sensors that detect issues such as overheating motors, door malfunctions and mechanical wear identified through noise or induced vibration. This data is collected and analysed to alert technicians to anomalies, prompting a response when deviations occur.

The primary advantage of maintenance monitoring is the ability to detect problems before they result in total failure. However, this approach remains largely “reactive” in that it does not forecast potential failures but rather responds once symptoms arise. As a result, while it improves efficiency compared to traditional scheduled maintenance, it does not fully optimise lift preventative maintenance.

Predictive maintenance: A proactive approach  

Predictive maintenance, on the other hand, leverages data analytics, historical performance records, and machine learning algorithms to “forecast” when components might fail. By analysing patterns in operation, predictive maintenance estimates wear and tear rates, enabling technicians to intervene before issues emerge.

This approach is said to significantly reduce downtime and costs associated with emergency repairs. Since maintenance is performed “only when necessary”, rather than on rigid schedules, it also optimises resource allocation, as the lift is not out of action unless required for short maintenance, and wait times for parts is zeroed. A lift's critical components, such as suspension ropes, winding machines, and door operators are continuously analysed through placement of sensors to anticipate potential failure points, minimising unexpected breakdowns.

How AI enhances predictive maintenance  

The integration of AI into predictive maintenance is said to be able to revolutionise lift management. AI-driven systems can process vast amounts of sensor data, learning from operational trends to refine failure predictions. Machine learning models improve accuracy over time, identifying complex failure patterns that traditional methods might overlook.

Additionally, AI enables “real-time analysis”, ensuring that predictive maintenance becomes even more precise and adaptive to changing conditions. Instead of relying solely on predefined statistical models, AI dynamically adjusts predictions based on live data from lifts in various environments.

As AI technology continues to advance, predictive maintenance could further reduce costly disruptions, improve safety, and enhance user experience, leading to more reliable and efficient lift operations worldwide.

The future or just an additional cost?

One question remains, will predictive maintenance or maintenance monitoring really be more cost-effective than traditional “hands-on” maintenance and improve the reliability of your lift stock? In reality, the question cannot yet be answered with certainty, as there is not enough data on the open market to back up some provider's claims about their predictive maintenance results. However, it does remain to be seen whether advances in AI technology could enhance maintenance monitoring and predictive maintenance, but at what cost? And will standards need to be enhanced to ensure passenger safety is not ignored?

The cost quandary

Several factors are going to influence the cost of predictive maintenance and maintenance monitoring. Whether this will increase or decrease the overall maintenance cost or make the contract premium more accurate is yet to be seen.

An unusual factor to consider is the client willingness to pay for parts replacement prior to their failure. In other words, will they embrace a proactive maintenance approach rather than waiting until a breakdown occurs? Such an approach anticipates issues before they arise, potentially minimising downtime and preventing larger, costlier problems in the future. By considering pre-emptive investments in reliability, clients might view these expenses as a smart, strategic move that ultimately saves money and headaches over time.

Other factors that will impact the maintenance contract costs are:

• Reduction in the number of visits by an engineer for either planned maintenance visits or unplanned interventions
• Cost and setup of installing sensors and interfacing with the equipment
• Subscription costs to the client enable lift owners to review their lift data on the maintenance provider's portal
• Some lift manufacturers won't allow interfacing with third-party equipment (sensors) so the lift owner may be tied into using proprietary equipment