Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities

Abstract

The increase in available data from sensors embedded in industrial equipment has led to a recent rise in the use of industrial predictive maintenance. In the aircraft industry, predictive maintenance has become an essential tool for optimizing maintenance schedules, reducing aircraft downtime, and identifying unexpected faults. Despite this, there is currently no comprehensive survey of predictive maintenance applications and techniques solely devoted to the aircraft manufacturing industry. This article is an in‐depth state‐of‐the‐art systematic literature review of the different data types, applications, projects, and opportunities for predictive maintenance in this industry. The goal of this review is to identify, and highlight the challenges and opportunities for future research in this field. This review found that the current focus of research is too biased towards aircraft engines due to a lack of publicly available data sets, and that greater automation is an important step to optimize aircraft maintenance to its full potential.

Enhancing aero-engine airway fault diagnosis through multimodal deep neural networks

“…With the development of multimodal data processing techniques, more and more studies are focusing on how to fuse different sensor data to improve diagnosis [14][15][16][17][18] . Some studies have explored methods such as feature fusion of sensor data and feature extraction after fusion.…”

Predicting Aircraft Engine Failures using Artificial Intelligence

“…Each cycle comprises the following additional variables in addition to the time series signals that are helpful in understanding the context of a flight cycle: the unit number, cycle number, a binary health state variable hs (set to 0 for unhealthy status and 1 for healthy status), and a categorical flight class variable Fc that represents the flight' length that is (set to 1 for short flights, 2 for medium flights, and 3 for long flights). Here, the simulated engines are operated past their optimal state until they eventually shut down [7]. 2) Data balancing: To address the issue of unbalanced data which is a challenging issue in machine learning, the Synthetic Minority Over-sampling Technique (SMOTE) has been opted in that sense.…”

Multi-modal LSTM network for anomaly prediction in piston engine aircraft

“…AI outperforms statistical methods in predicting equipment failure due to its ability to learn patterns from data and conduct predictive analysis [15] . Prediction algorithms based on machine learning and deep learning are capable of detecting latent data correlations and managing high-dimensional and multivariate data in complex, dynamic scenarios [16] . This study explores how can these algorithms be effectively utilised to predict engine flameout in piston engine aircraft, specifically using engine oil and cylinder head temperatures from a Textron Lycoming IO-540 six-cylinder piston engine widely used in small private and trainer aircraft?…”