Predictive Maintenance of Aircraft Engine using Deep Learning Technique
Ade Pitra Hermawan, Dong-Seong Kim, Jae‐Min Lee
Abstract
In this paper, an accurate algorithm to estimate remaining useful life of aircraft engine is proposed. Since the aircraft engine has a low fault tolerant, meaning that a little faulty in the system can lead to catastrophic conditions, an accurate and real-time information about the engine condition is required. This paper utilizes the combination of CNN and LSTM algorithms in learning the behavior of the historical data and providing the accurate information about the time to failure of the system. The simulation results demonstrate that the proposed system is able to achieve improved performance in terms of accuracy rate and computing time compared to the previous works.
Topics & Concepts
Computer scienceFault (geology)Search engineAero engineArtificial intelligenceEngineeringInformation retrievalGeologySeismologyMechanical engineeringFault Detection and Control SystemsMachine Fault Diagnosis TechniquesIndustrial Vision Systems and Defect Detection