Summarise with AI:
Many of the traditional time-based and condition-based maintenance approaches are no longer enough to address current aviation maintenance needs. Meanwhile, the industry is constantly dealing with supply chain pressure and workforce shortages. Among several developments in maintenance, predictive maintenance has drawn great attention from maintenance planners.
AI-enabled predictive systems and SaaS solutions are being used to address some of the capability gaps caused by workforce shortages by supporting maintenance teams with analysis and other tasks. This can help MROs handle key maintenance challenges without relying solely on adding more professionals.
This article looks at the role of AI-powered systems in predictive maintenance and examines what these systems are actually capable of today.
Key Components of Effective Predictive Maintenance
Some teams may rely on sensor readings, as you would see in airlines, aircraft MROs, or manufacturing companies. Smaller MROs may have years of inspection reports and repair records to work with.
- Historical data: A component may, for example, show a repeated pattern of being repaired or replaced after a certain period of use. Historical data may include repair dates, replacement intervals, operating hours, findings, failure records, and maintenance actions, though data will vary by business. These records help maintenance teams identify signs useful for predicting future problems.
- Condition monitoring: It involves regularly collecting information (such as vibration, temperature, pressure, oil condition, or other equipment-specific readings) about an asset. Rolls-Royce’s Engine Health Management system is one such example: it provides ground teams with a more detailed view of engine behaviour and supports decisions aimed at maintaining engine availability.
- Data collection and integration: Today, businesses rarely use a single system. On average, 91% of businesses use at least three applications to manage maintenance operations, while 55% use at least five. As a result, information is split across several systems. And they may not talk to each other. Data integration refers to connecting different data sources to get a complete view of their equipment and improve their maintenance strategy. After all, a maintenance strategy is only as strong as the data behind it.
- AI and machine learning: AI and machine learning are heavily used to analyse data coming from different systems – all of it manually at the same speed and scale is practically impossible. AI can continuously analyse incoming data around the clock.
The Role of Software
Behind the maintenance decision across organisations worldwide, an important change is happening. Owners are actively embedding AI and machine learning into the decision layer that helps maintenance teams make sense of data that would be difficult to analyse manually. AI is beginning to act more like an agent with the ability to mimic human intelligence than a tool.
It can process a mountain of data, helping teams to make more accurate decisions and reducing the amount of manual time required from hours to minutes, or even seconds.
This is visible in industry adoption. AI adoption for predictive maintenance has increased. Here’s how modern systems combined together are transforming the maintenance approach. Consider a component that has been repaired several times. With traditional methods, history exists in part in several systems. Now, if we have to analyse data from hundreds of thousands of components, it will require a considerable amount of time and personnel.
Modern systems are becoming part of the maintenance strategy itself. They provide the connection between what the equipment is doing now, what has happened to it before, and what the maintenance team needs to do next. The technology is powerful, but its usefulness ultimately depends on whether the systems underneath it can provide enough reliable context for that power to be used.
Modern MRO software does more than store maintenance records. It lays down an operational layer that connects historical activity, current condition, inventory availability, and planning activities. When these sources are poorly connected, maintenance teams may be pulled into low-value tasks. By consolidating data, software provides the visibility needed to support maintenance planning and more effective predictive strategies.
Use Cases of Predictive Maintenance in Aviation
1. Predicting and addressing recurring issues
Skywise, a predictive system developed by Airbus in collaboration with Palantir Technologies. It is being used by major airlines, including AirAsia, Delta Air Lines, and Emirates. It allows companies to optimise maintenance activities. EasyJet, by deploying the same platform, was able to handle operational problems that were causing the most disruption to its fleet.
2. Plan workshop capacity
MROs can more precisely estimate the workload, helping planners decide how much technician capacity will be required. Take an example of ForeSim-BI, a platform using data from 100+ aircraft maintenance projects, reported to have successfully predicted the maintenance workload.
Predictive maintenance can help an MRO move from reacting to incoming work to preparing for work that is likely to arrive.
3. Prepare parts before the work order
A predicted maintenance event can also become an inventory signal. If a component or consumable is likely to be required, the MRO can check stock and documentation, reserve available material, or start procurement before the aircraft arrives, or the component reaches the workshop.
4. Predict component maintenance
The Turkish Technic study is a good example of predictive insights. The company developed predictive models using 10 years of maintenance logs and records, including part numbers, serial numbers, installation dates, flight hours, and cycles to forecast future maintenance events. The models estimate the likelihood and timing of future maintenance events for components based on their historical records.
Final Words
Predictive maintenance is not new to aviation. Current technology enables decision-makers who have always made predictions from experience to make data-driven decisions. Modern systems can now analyse far more data than a person could reasonably review.
These systems open up a more practical way to use data to make practical decisions. For MROs considering adding predictive capabilities, the starting point does not have to be a large technology project. A SaaS application can provide a practical foundation. If you are evaluating how predictive capabilities could fit into your existing MRO workflows, talk to our team about your current maintenance processes and requirements.
