In 2019 alone, US airlines reported 302 tarmac delays longer than three hours, compared with 202 in 2018 and 193 in 2017, according to statistics by the US Bureau of Transportation. One of the main causes of such delays is unplanned maintenance operations. Technical issues in airplanes can take a long time to identify and resolve, causing major financial losses to airlines, and delays that can have various consequences for their customers. To address this issue, airlines are looking for ways to incorporate artificial intelligence (AI) into their maintenance operations. The use of AI is expanding as a decision-making tool for maintenance teams at commercial airlines. From predicting breakdowns to guiding on-field line maintenance teams, these are some of the ways airlines are using AI to optimize their maintenance procedures.
Planning and scheduling maintenance tasks
Most of the maintenance operations in airplanes are planned. Most parts on an aircraft have a known ‘expiry date’ when they must be replaced; it could be after a specified amount of flight hours or when they exceed a certain tolerance. Maintenance teams in airlines need to keep track of all planned maintenance tasks to ensure that planes are always operating at optimal levels and to reduce the chances of unplanned maintenance issues popping up when they least expect it. However, this is easier said than done. Planes have thousands of components that can be hard to keep track of, especially for large fleets. This is where AI comes in. AI-powered systems can keep track of every single component in each plane to generate specific work order tasks for maintenance technicians and ensure that all resources required during maintenance operations – including manpower, spare parts and hanger slots – are prepared and allocated well in advance.
While unplanned maintenance operations are not as common as planned maintenance operations, they can have terrible effects on airlines. For one, unplanned maintenance accounts for 30 percent of total delay time in airports, which costs airlines millions in revenue lost annually. Using AI-powered analytics, airlines can detect anomalies in planes and deploy predictive maintenance solutions long before a break-down occurs. The ability of AI to analyze large amounts of data can take preventative maintenance systems to new heights. By layering in additional data from aircraft health monitoring sensors, neural networks can enhance more traditional methods like using borescopes to examine engines. When properly deployed, the ability of AI to predict breakdowns and allow planned interventions can also help airlines reduce operating costs and downtime while improving production yield.
Coordinating maintenance operations
A lot of airline departments are involved during maintenance operations, from engineers to accounting, to customer service. All these departments need to work together to ensure that all maintenance operations are carried out with maximum efficiency. AI-powered workflow organization systems can be used to coordinate maintenance operations across various departments. Typically, these systems can be accessed from desktop and mobile devices, granting authorized departments access to real-time or historical data from any location through alerts, notifications and reports. Armed with this data, different departments can do their part to ensure the airline runs smoothly despite ongoing or upcoming maintenance operations. For example, the AI system can inform accountants of upcoming maintenance operations so that they can ensure that spare parts are ordered on time. Similarly, the system can inform customer service departments of any future flights that will be affected by maintenance operations so that they can reschedule flights accordingly.
AI deployments have brought new efficiencies to the aviation industry, especially in the maintenance and repair space. The continued implementation and progression of AI in aircraft maintenance operations will help airlines unlock new revenues, reduce aircraft on ground (AOG) time, decrease operational costs, and improve overall functionality.