March 2, 2024

A timely fix: Predictive Maintenance

Markus Gilges, Solutions Specialist at Collins Aerospace, has 20+ years of experience of addressing technical issues in and around aircraft cabins. Here he looks at Predictive Maintenance for the aircraft cabin...

Collins Aerospace is bringing Predictive Maintenance to aircraft interiors. New products – enabled by Artificial Intelligence (AI), edge computing, advanced sensor technology and real-time data transmission – are empowering cabin crews with real-time predictive information to enhance the travel experience.   

Considering the negative impact that faulty cabin equipment can have on the passenger experience, it is not surprising that airlines are embracing the concept of Predictive Maintenance for the cabin.

An unusable premium class seat, an unavailable lavatory or a dysfunctional coffee maker or oven can negatively shape the passenger’s perception of the airline through a reduction in catering service or a delay caused by an unforeseen repair. Collins is a world leader in aircraft cabin interiors, supplying airlines with everything from seating and galley equipment to lavatories, lighting and oxygen systems plus much more. 

The expansion of Predictive Maintenance (PM) into the cabin is not only a natural next step in enhancing the air travel experience but also can reduce aircraft downtime spent on repairs and can create fleet-wide maintenance efficiencies, contributing to airline operability and cost savings. At the 2023 Aircraft Interiors Expo in Hamburg, Germany, Collins showed some of their solutions in that area. 

What is Predictive Maintenance?  

PM takes historical aircraft and maintenance data and uses it to develop performance trends for components. Collins’ Connected Aviation Solutions (CAS) business is developing Predictive Maintenance analytics using a multitude of relevant parameters for different components or systems to predict future performance.   

The PM system then generates detailed maintenance recommendations for the airline which may help prevent maintenance-related delays, flight returns or unpleasant Aircraft on Ground (AOG) situations. This may also enable the airline to turn unscheduled maintenance into planned maintenance.    

To achieve this, PM uses artificial intelligence and machine learning to analyse thousands of parameters, as well as look for correlations between parameters in the more complex systems that have multiple potential failure points. Because of the inbuilt intelligence, the system learns and can fine-tune algorithms and its predictive capabilities with every additional data package that it analyses.      

Another aspect of Predictive Maintenance that becomes increasingly important is the need for data interoperability. This is especially relevant when considering the growing number of components to which Predictive Maintenance is applied.

Airlines tend to have many different system providers onboard the aircraft – cabin or otherwise – all of which are becoming ‘smarter’ by producing more data for maintenance purposes. For a Predictive Maintenance provider like Collins, this means that data integration and interoperability are key to developing a holistic analytics portfolio.

Data gathering   

For an airline to have multiple Predictive Maintenance systems is highly ineffective, which is why it is essential for Predictive Maintenance providers to gather relevant data on the components of various suppliers. This requires a shift from the old paradigm of singular solutions where no data is generated or shared. Openness and a collaborative spirit is now needed for Predictive Maintenance.   

Predictive Maintenance may also take into consideration various environmental data. Think of the different operating environments for an airline’s air conditioning system on the ground in the Sahara or in Greenland

External factors such as temperature and humidity can impact air conditioning system usage, and, subsequently, its maintenance requirements. Similarly, Collins’ recent acquisition of FlightAware enables the company to access a wealth of aircraft operational data, which it can use to increase the accuracy of its predictive maintenance solutions. 

Impacts of PM

There are three key areas where the impact of PM will be noticed: 

Financial impact  

Although Predictive Maintenance can reduce the occurrence of negative events, it is sometimes difficult to quantify its benefits. Use cases for a variety of components show the value predictive maintenance is creating through avoided AOGs, aircraft turns, delays, cancellations, reduced maintenance costs and also lower spare parts stock for components. 

Think of a Premium class seat that is inoperable because the recline mechanism is broken. If this issue were discovered just before the aircraft’s scheduled departure, the airline would have to block that seat, not only costing the airline revenue and service costs but also impacting a passenger’s trip. Predictive Maintenance, however, could identify the faulty recline mechanism before it occurs, allowing operators to address the issue during scheduled aircraft downtime.  

Organisational impact 

Collins Aerospace Ascentia is a unifying data management platform for aircraft and fleet health monitoring, bringing together various sources of data throughout the organisation. One of its main purposes is to enable the airline to use its data more effectively. It is general knowledge that the increase in operational- and maintenance-related data produced by the most recent generation of aircraft is orders of magnitude bigger than the previous generation.  

Through various self-help tools in Ascentia, Collins aids airlines to navigate and use these huge amounts of data. The easy-to-use, platform-agnostic, web-based infrastructure enables the airline to create new types of maintenance reports in-house in an afternoon rather than having to wait for the component original equipment manufacturers (OEMs) to create such reports in months.  

Impact on sustainability   

Predictive Maintenance also has an impact on sustainability. An example is the detection of ailerons – the little flaps on the edge of wings – being out of rig. Traditional aircraft maintenance will usually only detect an issue if the aileron has reached a minimum threshold for being out of rig. 

That means that the aircraft could be flying with sub-optimally adjusted ailerons for a period of time. This won’t affect the aircraft’s safety but it increases fuel consumption. PM enables the airline to detect an ‘out of rig’ situation much before the normal maintenance procedure notification comes in.   

The increasingly connected aircraft and the resulting exponential growth of data will enable airlines to generate information which is relevant to its operation and maintenance at a level that was unheard of a few years ago. This will create a substantial potential for the utilisation of Predictive Maintenance to optimise the airlines’ performance and the passenger experience. 

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