Application of Deep Learning in Predictive Maintenance of Aircraft Engines

Abstract

The performance and dependability of aircraft engines are vital to the aviation sector, which is a key pillar of the world economy. Ensuring the seamless functioning of these engines is crucial for both economic efficiency and safety. While reactive and preventative maintenance are examples of traditional maintenance systems, they have limits of their own. Preventive maintenance can save costs by avoiding needless inspections and part replacements, whereas reactive maintenance frequently results in unplanned outages. In this regard, deep learning-powered predictive maintenance shows up as a ground-breaking method for raising the dependability and effectiveness of aircraft engines.

Predictive maintenance, or PdM, uses a variety of data sources to monitor the state of the equipment and make maintenance recommendations. Instead of using preset plans or scheduling maintenance after a breakdown has occurred, this technique seeks to prevent failures and optimize schedules depending on the actual state of the equipment. Deep learning (DL), a branch of machine learning, is modeling complicated patterns in huge datasets by using multiple-layered artificial neural networks (thus the term "deep"). It is very effective at time-series data analysis, picture identification, and natural language processing, which makes it appropriate for predictive maintenance jobs involving substantial volumes of sensor data.