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Highlights
- Generative AI (GenAI) can enhance decision making, process optimization and automation in MRO functions by analyzing patterns in historical data and applying that understanding to new datasets, thereby generating unique insights.
- GenAI can offer both analytical as well as logical conversational assistance to execute MRO tasks. It aids in transitioning human-centric MRO M&E systems to a technology-driven ecosystem.
- By leveraging the problem solving and decision-making capabilities of GenAI, MROs can try for maximum customization of LLM models to address their specific and distinct challenges to arrive at the right kind of solutions for a range of aircraft maintenance scenarios.
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The GenAI promise
The aviation sector is expected to see robust growth in the coming decade, which means MROs are going to get increasingly busy.
To be able to deliver superior aircraft maintenance services and ensure zero schedule slippages, MROs will need to expand their resources and capabilities considerably. This calls a transformation of the aircraft maintenance ecosystem, underpinned by technologies such as artificial intelligence and machine learning (AI-ML), data analytics, GenAI, digital twins, 3D printing, and more.
As GenAI begins to disrupt businesses and life as we know it, MROs must prepare to put this technology to good use, delivering manifold benefits for themselves as well as their customers. Traditional AI or predictive AI assists in process excellence such as predictive maintenance, through understanding the underlying patterns and responding to new data sets in the same manner as it is trained. On the other hand, GenAI can understand underlying patterns in the data it is trained on, apply that analysis to new datasets (which can be either labelled or un-labelled) and generate totally different yet meaningful outputs.
Given the remarkable insights it can offer GenAI can be used across a wide variety of MRO use cases to drive effective decision making. This technological marvel can aid a business in various ways such as virtual assistance and chatbots, reducing the need for extensive skill sets required for complex aircraft maintenance and operations.
Expedite maintenance activities, ensure business continuity
Managing the airworthiness of aircraft through regular maintenance, while generating revenue through efficient operations, is anything but easy for MROs.
Aircraft maintenance involves a lot of critical decision-making processes, which must be based on accurate insights and observations as they have sizeable financial implications and have a direct bearing on the safety of people. GenAI models can be trained to analyze huge amounts of structured and unstructured data to aid these processes, while reducing the dependence on manual discretion considerably.
These models can offer faster and more dynamic solutions for MROs in resolving critical situations such as aircraft on ground (AOG), which have monetary implications, disrupt operations, and inconvenience passengers. With GenAI aiding quick resolutions in such cases, MROs will be able to release aircraft to service a lot faster, thereby improving availability and operational efficiency. GenAI can be fine-tuned to assist maintenance personnel across functions such as material planning, inventory control, and reliability by providing contextual insights to nourish their decision-making capabilities.
Report intelligently, drive regulatory compliance
It is critical for aircraft operators, lessees, lessors, and OEMs to strictly adhere to the airworthiness regulations set by regulatory authorities to ensure continuous and safe operations.
Airworthiness Directives (AD) are regulations issued by authorities such as the US Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA) based on findings and observations that may affect the operability of in-service aircraft. These directives are intended to alert the airline or MRO regarding concerns around safety of a particular aircraft type, model, engine, or other systems. Non-compliance of an affected AD on certified aircraft by the compliance due date will result in severe operational consequences.
GenAI can be adopted by MROs for managing ADs through their lifecycle – from origination to termination – much more effectively and dynamically. MROs are required to track and report the AD status, right from its effective date through due date, for all affected aircraft, and any non-compliance can adversely affect operations and damage the carrier’s brand.
Additionally, GenAI solutions can assist in generating and enterprising AD reports along with insightful suggestions and maintenance solutions. They will be of great help here as it can download ADs from the huge repository, distributing them among affected aircraft with relevant parameters, monitoring, reporting as well as alerting, along with pre-empting and scheduling maintenance activities.
Offer real-time assistance, improve personnel productivity
The key objective of MROs is to ensure an aircraft is serviceable and operational through most of its life, with minimal downtime and delays at any stage.
GenAI can notably contribute here, by offering remote chat assistance to field personnel. Given the ability of large language models (LLM) models to create contextual text, images, and videos from underlying data based on various prompts, they can act as informed chatbots providing contextual information to the MRO workforce.
These interactive chatbots can save technicians the time and effort they typically have to spend in laboriously looking through reams and reams of documents to derive contextual insights from a large repository of historical maintenance data. GenAI’s language translation capabilities are incredibly powerful, allowing personnel to communicate easily in the language of their choice, which makes the maintenance process a lot easier and more efficient. LLM models can also be trained to amend or append the underlying documents when prompted.
Cluster repetitive defects, keep systemic failures at bay
It is critical for MROs to tend to recurring fault messages and warnings, as they could point to possible flaws in a component or system.
Aircraft safety architecture is designed to provide fallback systems and robust warning mechanisms for additional security and to minimize disruption during operations. The repeated warnings and fault messages displayed on the aircraft cockpit display units might be considered as a potential threat, which mandates a detailed root cause analysis of the nature of these defects, as well as troubleshooting and conducting the maintenance action to resolve an anomaly.
GenAI models can be used to understand the underlying data patterns to group these defects and aid the root cause analysis as well as the maintenance activities that need to be performed. LLM models can cluster these defects based on their nature and other parameters, providing MROs a high level of visibility and deep insights about potential failures that can hinder the integral functionality of various systems and components.
To conclude, although real-world use cases are evolving, but GenAI most definitely holds great promise in the field of aircraft maintenance. As GenAI paves the way for a bright future, industry players are working on developing a range of capabilities using this technology to cater to real-world MRO M&E scenarios, some of which will surely see implementation in the near future.
