Aircraft on Ground (AOG) – a dreaded phrase in the aviation industry. Every minute of AOG leads to lost revenue and operational disruption – in fact, it’s estimated that a single AOG event can incur costs ranging from USD 10,000 to USD 150,000 per hour of downtime.
The best solution to AOG scenarios is to prevent them before they happen – and one of the most promising strategies to achieve this is predictive maintenance (PdM). This article explores how helps.
AOG Scenarios and Their Impact
AOG events often force airlines to reaccommodate passengers, arrange crew overtime or rest, and sometimes source replacement aircraft, all adding to expenses.
Repeated AOG issues hurt on-time performance metrics and customer confidence, and in the EU, extensive delays can incur compensation payouts under regulations like EU261, further increasing costs.
In terms of Maintenance Repair and Overhaul processes, AOG situations create intense pressure to respond quickly. Parts need to be rushed to the grounded aircraft, and maintenance teams aim to diagnose and fix the issue as fast as possible.
Naturally, preventing such crises in the first place is preferable, and this is where predictive maintenance comes in.
From Reactive to Predictive Maintenance
Predictive maintenance involves continuously monitoring the actual condition and performance of equipment in order to foresee issues. Through the continuous analysis of sensor readings, flight history and maintenance records, patterns emerge that can indicate an upcoming problem.
Essentially, PdM enables airlines to move unscheduled repairs into scheduled maintenance windows, which means fewer AOGs and delays, a better passenger experience, and lower costs overall. It’s been said that the goal of PdM is to “turn an unplanned event into a planned event”.
easyJet also describes a situation in which a predictive approach allowed them to detected a fault before an AOG event occurred. In this instance, a fault was identified in an engine’s low-pressure fuel pump actuator before any warnings were triggered. Engineers then confirmed that the part was deteriorating. Existing data suggested that if the part had failed, there would be a 78% chance of an AOG event lasting more than three hours.
How Does Predictive Maintenance Work?
Data Processing
Handling the data requires robust technological infrastructure. Aircraft transmit real-time telemetry data either continuously via ACARS/SATCOM or upon landing via quick access recorders and wireless connections. The data is then passed to cloud solutions where it’s stored and analysed.
Manufacturers have developed centralised Aircraft Health Monitoring Systems (AHMS) that collect high-frequency sensor readings from key systems (engines, avionics, landing gear, etc.) and alert ground engineers to any anomalous condition.
Airlines can also use independent or in-house analytics systems. In all cases, the technology stack typically involves streaming data pipelines, data repositories that aggregate years of historical information, and machine learning models that flag potential failures in advance.
Predictive Models
The predictive models look for patterns or anomalies indicative of emerging faults. This might include subtle vibration pattern changes, temperature or pressure drift, or other performance degradation trends that a human might miss. As these models ingest more data over time, they learn and improve their accuracy, refining their ability to forecast failures well before a part actually breaks.
Prescriptive Maintenance
Predictive maintenance systems not only identify what might fail and when; they can also generate actionable recommendations, which has given rise to the term ‘prescriptive maintenance’.
With this insight, maintenance teams can coordinate the pre-emptive fix during scheduled downtime, thereby avoiding an AOG that would have occurred had the part run to failure.
Financial and Operational Benefits of Predictive Maintenance
Cost Savings on Maintenance and Operations
Addressing issues early means airlines and MROs avoid the higher costs associated with major failures, reducing overall aircraft spend.
The U.S. Department of Energy has reported that predictive and prescriptive maintenance strategies can reduce maintenance costs by up to 30% on average.
Predictive maintenance also helps identify faults that are causing excessive fuel burn, helping airlines save on fuel costs.
Extended Asset Life and Performance
Parts are serviced based on condition, not just a conservative schedule, which can defer unnecessary maintenance without compromising safety. At the same time, catching issues early, as mentioned, can prevent escalation that might damage the asset. Overall, aircraft and components can have longer lives and less severe failures.
Better Parts Inventory Management
Forecasting models help optimise parts availability so that parts are neither under-stocked (causing delays) nor over-stocked. This leads to leaner inventory, faster turnaround times and lower operating costs.
Higher Labour Productivity
Maintenance teams can work more efficiently when guided by predictive insights. Rather than spending time on routine checks that often find nothing wrong, technicians can focus on the specific items the analytics have flagged.
Research by Deloitte has shown that predictive maintenance can improve labour productivity by 5-20%, which is important today as workforce resources are stretched thin.
Data-Driven Continuous Improvement
Each fix and each prediction outcome provides feedback that makes the system more intelligent. Over time, operators build a comprehensive database of failure signatures and maintenance actions, which helps in improving maintenance programmes – and engineering designs, if the data is shared with manufacturers.
Implementation Considerations
Implementing PdM is not without challenges. Industry experts and recent research point out several key obstacles that airlines and MROs must overcome.
Data Availability and Quality
Effective predictive modelling requires large volumes of high-quality data, but getting that data can be difficult as it can often be siloed in different systems.
In some cases, OEMs have considerable control over data sharing frameworks and the level of access and control that other parties have can be complex. As such, airlines and MROs need to cooperate with OEMs to make predictive maintenance a viable part of their fleet management strategy.
Even when data is available, it might contain errors, missing fields, or inconsistencies from different sources, leading to unreliable predictions. Cleaning and harmonising this data is a significant undertaking.
Analytical Complexity
NASA cites the complexity of prediction as a barrier to the widespread adoption of PdM. Aircraft are intricate machines with many interdependent components, and failures can stem from multiple factors. Creating models that accurately predict failures – with minimal false alarms or missed detections – is challenging.
It requires not only machine learning expertise but also deep engineering knowledge to choose the right parameters and interpret the results. What’s more, many failure modes are relatively rare; of course, this is a good thing for safety, but it means less data to train on.
Other factors causing complexity relate to the operational environment; atmospheric conditions and pilots’ control of the aircraft are hard to factor in.
With PdM, there’s also a risk of false positives, which could lead to unnecessary maintenance, or false negatives, which undermines trust in the system. Finding the right balance is an ongoing effort.
Compliance and Safety Standards
Despite the benefits, regulators are cautious about predictive maintenance; they need to be convinced that a new data-driven approach is as safe as (or safer than) established practises.
This means airlines and MRO providers must thoroughly validate predictive algorithms and possibly run them in parallel with traditional maintenance processes for some time to collect evidence of their effectiveness.
How OASES Aviation MRO Software Supports Predictive Maintenance
Our cloud hosted MRO software is a unified platform that standardises every element of MRO operations. This includes the scheduling of line maintenance and heavy maintenance, inventory control, compliance monitoring, the tracking of maintenance tasks, financial management, warranty management and more.
In terms of PdM, OASES consolidates data from electronic flight bags (EFBs), e-enabled aircraft, maintenance logs and flight operations systems. It provides a single, cloud based repository for all relevant aircraft and maintenance data, eliminating silos. This ensures a unified view for analysis and decision-making.
APIs including OASES Gateway and OASES Workflow give users more flexibility than ever when exchanging data with third-party systems.
Conclusion
Predictive maintenance has become a key strategy for keeping aircraft flying and avoiding dreaded AOG scenarios. Implementing PdM isn’t the simplest task, but organisations that have done so are reaping significant rewards. When aviation regulations adapt and the industry overcomes prediction complexity, perhaps PdM will become the standard.
To learn more about how OASES enables streamlined workflows and boosts operational efficiency and profitability, contact us today.