This blog explores a trend called predictive maintenance in the aerospace industry. It discusses what predictive maintenance is and the current trends, the applications of predictive maintenance, how it works, the benefits, and the underlying challenges.

Predictive Maintenance: A Brief Overview

The aerospace industry is highly cost-intensive, where time is money. In such an industry, maintenance of assets is a highly challenging task. Traditionally, professionals conduct aerospace asset maintenance physically where they check all the components manually, which results in a waste of time and resources. Statistics show that 25% of the flights in the United States experience delays due to a lack of proper maintenance standards.

Predictive maintenance, (which is widely leveraged in other industries), can also help the aerospace industry counter this problem. It comprises collecting real-time data from sensors attached to the aerospace assets, after which the data is analyzed with the help of a machine learning algorithm. Based on this analyzed data and historical data, a prediction is given. This prediction helps the maintenance professionals to find the location of the problem, thereby reducing the reaction time.

Applications of Predictive Maintenance

The definition, process and benefits of predictive maintenance have been dwelt upon till now. We understand the real-time applications of predictive maintenance in the aerospace industry. eInfochips brings deep domain expertise in Aerospace & Defense, delivering end-to-end solutions that integrate sensor technology, data pipelines, and predictive intelligence for flight-critical systems.

• Satellite Monitoring: Sensors attached to the satellite record the parameters regularly and transfer the data to the control center. The data is then analyzed and a prediction about the performance of various components is given.
• Drone Monitoring: Various battery parameters like charge cycles, temperature, and voltage are analyzed to help predict their future performance. This analysis reduces the risk of mid-flight failure in drones.
• Components Manufacturing: Some aerospace components are manufactured using 3D printing. Sensors embedded in the parts record their real-time stress and fatigue data. This data is analyzed after the tolerance of these parts is known. This metric determines whether any design changes are required.

Predictive Maintenance at Work

After employing this standardized solution, many aerospace companies witnessed improvements in maintaining the quality of aerospace assets. Let us take a look at the working of predictive maintenance by considering an example.

Consider the case of engine monitoring in the aviation industry.

• Data Collection: This is the first and most crucial step. Data from FADEC and engine health monitoring sensors, such as temperature, pressure, vibration, and rotor speeds, is collected during the flight and transmitted via systems like ACARS or SATCOM to ground-based platforms. All the data from the engine is collected through the sensors in real-time and stored in on-board systems.
• Data Transmission: The real-time data now goes through a transmission phase, where it is uploaded into a cloud-based analytics platform. In modern aircraft, this data is transmitted via satellites to the control center. Preprocessing steps filter the noise, normalize conditions, and extract the trend-able features.
• Data Processing/Analytics: This is the step where the machine learning algorithm takes as input all the real-time recorded data, and this data is then analyzed. After the analysis, the machine learning algorithm compares this data with:
  • Historical engine performance data
  • Known patterns of failure
  • Manufacturer threshol
• Then, the final prediction is given. Advanced models, including physics-based simulations, statistical forecasting, and machine learning algorithms like LSTM or Random Forest, detect anomalies and predict component degradation or remaining useful life.
• Post Prediction Action: Insights trigger early alerts, enabling airlines to schedule inspections or part replacements before failures occur, thereby minimizing unscheduled downtime. This is the last step which is conducted by asset maintenance professionals. Based on the prediction given, they know exactly where, and which part of the engine requires maintenance. Continuous feedback from maintenance logs helps refine these models, improving accuracy over time. As a result, this process reduces any time and money wastage.

This approach enhances operational efficiency, safety, and cost-effectiveness by shifting from reactive to predictive engine maintenance across commercial and defense fleets.

Benefits of Predictive Maintenance

Predictive maintenance offers significant benefits to aerospace OEMs, as demonstrated by engine monitoring alone. For instance, when an engine crosses a predefined threshold, early warnings can trigger timely interventions to prevent costly failures.

• Cost-effective: The adoption of predictive maintenance is cost-effective as it helps avoid unscheduled repairs. After monitoring all the engine parameters and after getting the prediction, maintenance professionals know that the vibrations in the engine need to be taken care of. Now, the necessary maintenance is done only to reduce vibration. This way unnecessary maintenance on the other engine parameters can be avoided.
• Safety Assured: Safety is one of the major concerns for companies in the aerospace sector, especially commercial aviation. In this example, by taking in data from the sensors, predictive maintenance helps the maintenance professional know exactly that the vibrations in the engine are the only concern. With this information in hand, companies could foresee serious threats to the engine and could avoid the problem of sudden failure.
• Assets Living Longer: Instead of replacing the engine components as and when needed, predictive maintenance gives complete information on exactly when and what component (in this case, the excess vibrations) needs maintenance at a specific time. This process reduces the cost of maintenance and extends the life span of the existing asset components.

Challenges to Adopt Predictive Maintenance

One would ask, if predictive maintenance is a solution to crucial problems related to maintenance in the aerospace sector, why don’t all companies adopt this procedure? The answer is that even if predictive maintenance has many benefits, adoption of this procedure comes with many challenges. They are:

• High Initial Investment: The sensors, the IoT infrastructure, and the data management platforms that make predictive maintenance possible require significant upfront investment.
• Complexity of Aerospace Systems: The complexity of systems on aerospace assets is increasing rapidly. As a result, modelling failure patterns require deep domain expertise and high-quality historical data, which may not always be available.
• Regulatory and Certification Hurdles: Aviation is a highly regulated industry. Predictive maintenance tools must meet safety and compliance standards. Therefore, gaining approval for AI-based maintenance decisions can be a lengthy and complex process.

Predictive maintenance is a transformative force that reshapes the aerospace industry by endowing the ability to foresee component failures, thereby enhancing safety, reducing costs, and maximizing operational efficiency.