Fix it before it breaks: Predictive maintenance

Predictive maintenance is a proactive maintenance strategy that evaluates and predicts when a component might fail so that maintenance work can be scheduled at a time that minimises adverse impact.

It differs from reactive or preventative maintenance in that the predictions are based on the exact condition of the equipment rather than average or expected life statistics. In addition, the maintenance teams no longer have to wait for the flight to land to capture data – predictive maintenance happens in real time, enabling workers to analyse data insights and algorithms while the aircraft is still airborne.

This precision is proving invaluable and is creating a paradigm shift in military aircraft maintenance. With military aircraft undergoing some of the most rigorous tests and experiences, equipment failure cannot always be anticipated, nor might the scheduled regular maintenance be enough to keep assets in best form. Predictive maintenance helps minimise malfunction and failure during operations while also lowering operational costs and increasing aircraft availability.
The initial set-up of predictive maintenance is costly. However, the long-term benefits are significant – replacing a failing part is far more cost-effective than overhauling one after a full failure, and the overall benefits to fleet safety are incomparable.

Aircraft Health Management (AHM) adds an extra layer of confidence because it goes beyond just predicting and replacing components – it allows to back-manage part availability and distribute resources accurately, resulting in holistic inventory management. In the military, it is all about force readiness, and having an aircraft out of action or unavailable because of unplanned maintenance is a loss financially as well as in terms of mission effectiveness.

In fact, the United States Department of Defence has set a goal to achieve “zero unplanned maintenance” through adopting predictive algorithms, as announced by the U.S. Air Force officials on 9 April 2019. As countries maintain and modernise their fleets and seek greater technological integration, aircraft Maintenance, Repair and Overhaul (MRO) companies are under continuous pressure to be as efficient as possible.

MRO companies are increasingly playing a pivotal role in the sustainment of aircraft using data analytics, and in supporting Original Equipment Manufacturers (OEMs) with the aggregated data they need to adapt their designs, products and assets. Though OEMs hold test data to provide insight into aircraft maintenance needs, it is the operational and maintenance data held by MRO entities that is usually the most valuable for predictive maintenance, since it is based on actual flying experience. Although this domain lacks defined regulations around data ownership, a consensus on sharing for safety and efficiency first is driving coordination and collaboration in maintenance analytics.

The future MRO ecosystem seems to be steering towards OEMs turning into centralised technical data centres, with MRO companies offering invaluable insights. The need of the hour is better coordination 2 among the aftermarket industry in streamlining this effort and leveraging the highest possible value from OEMs as data insight becomes increasingly sophisticated with the integration and evolution of artificial intelligence, the Internet of Things and machine learning.

With this new trend in maintenance bringing the supply value chain partners closer together, establishing a unified operational framework or guiding regulations for the industry is becoming imperative so that we can operate in mutual benefit. If it were not for the repercussions of the COVID-19 pandemic, the future would have been brighter for the MRO industry, with predictive maintenance transforming the way in which aircraft health and sustainment are monitored. It remains to be seen how long this may take, and how far the power of data can push the sector into a new phase of growth.
Khalid Al Breiki – President – Mission Support – EDGE

Al Jundi

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