Aero engine health monitoring, diagnostics and prognostics for condition-based maintenance: an overview

Abstract

Aero engine performance deterioration highly influences its reliability, availability and life cycle. Predictive maintenance is therefore a key figure within Industry 4.0, which guarantees high availability and reduced downtime thus reduced operational costs for both military and civil engines. This leads to maintenance on demand and needs an effective engine health monitoring system. This paper overviews the work carried out on aero engine health monitoring, diagnostic and prognostic techniques based on gas path performance parameters. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality data base using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Generating dependable information about the health condition of the engine is therefore a requisite for a successful implementation of condition-based maintenance. Based on this study, further research can be attempted to predict residual life of critical components using degradation pattern from aero engine performance data bank which will be an invaluable asset for engine designers as well as for operators.

Construction of an Integrated Framework for Complex Product Design Manufacturing and Service Based on Reliability Data

“…This model not only realizes the integration of products and services, guarantees related functions but also provides an important foundation for the identification of key nodes and effective service configuration for availability. Rath N et al [36] believed that the deterioration of aero-engine performance has a great impact on the reliability, availability and life cycle of complex products, developed an engine health detection system and carried out research on engine health monitoring, diagnosis and prediction technology. The results show that acquiring, analyzing and utilizing engine health information is a must for condition-based maintenance, which ensures high availability of complex products and reduces downtime and operating costs.…”

An Integrated Monitoring, Diagnostics, and Prognostics System for Aero-Engines under Long-Term Performance Deterioration

“…A great variety of algorithms for each PHM stage have been separately developed in recent years, taking advantage of the progress in machine learning and deep learning research. Comprehensive reviews about this progress, such as [4][5][6][7], can be found in the literature. Recently, researchers have focused on deep learning methods such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in order to exploit their powerful feature learning and classification/prediction capabilities for use within PHM strategies in rotating machinery [8], in particular for aircraft engines.…”

A data-driven approach for health status assessment and remaining useful life prediction of aero-engine

“…In addition to fouling and erosion, other degradation phenomena are abrasion, corrosion, foreign and domestic object damage and increase in blade tip clearance [14]. The principal approaches of the EHM are diagnostics and prognostics [15]. Among the most exploited AI algorithms in the development of intelligent health monitoring tools, there are Artificial Neural Networks (ANNs).…”