Predictive maintenance is emerging as a necessity for aerospace and defense systems. By leveraging advanced analytics to monitor equipment health and anticipate failures, operators can transition to proactive strategies that maximize operational readiness, improve maintenance efficiency and reduce costs.
In this article, I'm focusing on mission-critical safety and security platforms in the aerospace and defense sector because of the high risks involved in system failure. In this sector and others, the cost of keeping a system up and running is more efficient and effective than waiting until something wears out or breaks down, so the lessons here are broadly applicable, too.
This proactive approach reduces costly unplanned maintenance and minimizes disruptions and cancellations that inconvenience passengers. Over time, predictive maintenance leads to lower overall operating costs for airlines by extending the usable life of aircraft components and systems.Implementing predictive maintenance requires building workforce capabilities that might not currently exist within many A&D organizations.
Software can be designed to take advantage of hardware features that provide data on the health of the hardware itself via IP that can detect potential memory or power failures, for example. Robust rollback and recovery mechanisms are also essential, allowing systems to revert to a known good state if issues arise during maintenance.
Digital twins provide a powerful platform for refining predictive maintenance algorithms, testing “what-if” scenarios and calibrating diagnostic techniques. They unlock capabilities for prognostics by simulating how potential faults cascade across integrated systems. Data from the physical asset flows into the digital twin, allowing it to mirror and track performance over time. The digital twin then generates refined predictive analytics to prescribe preemptive maintenance actions.
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