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Review
. 2023 Sep 27;23(19):8124.
doi: 10.3390/s23198124.

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Shuai Fu  1 , Nicolas P Avdelidis  1
Affiliations
Review

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Shuai Fu et al. Sensors (Basel). .

Abstract

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

Keywords: aircraft systems; condition-based maintenance; data driven model; hybrid model; physics-based model; predictive; prognostics and health management; remaining useful life.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The OSA-CBM (ISO 13374 [7]) functional block diagram (Source Mimosa).
Figure 2
Figure 2
Publication results with keywords ‘prognostic’ ‘aircraft’ ‘system’. (a) Documents by year. (b) Documents by sources.
Figure 3
Figure 3
Publication results with keywords ‘hybrid prognostic’ ‘aircraft’ ‘system’. (a) Documents by year. (b) Documents by sources.
Figure 4
Figure 4
Chronology of reportable accidents (rate per 100,000 flight hours) (from CAA, CAP 1145 Offshore helicopter review).
Figure 5
Figure 5
Classification of uncertainty in the prognostics.
Figure 6
Figure 6
Steps for determining prognostic metrics (Adopted from [20]).
Figure 7
Figure 7
Illustration of physics–based model.
Figure 8
Figure 8
Execution steps of a data-driven approaches.
Figure 9
Figure 9
Simplified view of a feedforward ANN.
Figure 10
Figure 10
Illustration of the data flow within a neural network, including the subsequent output layer.
Figure 11
Figure 11
Illustration of how DBSCAN and k-means clustering work. (a) Dataset contains two distinct clusters. (b) DBSCAN correctly identifies the two clusters with Euclidean distance. (c) DBSCAN correctly identifies the two clusters with squared Euclidean distance. (d) K-means clustering fails to correctly identify the two clusters using squared Euclidean distance.
Figure 12
Figure 12
Regression analysis classification.
Figure 13
Figure 13
Classification of prognostic models.
Figure 14
Figure 14
Hybrid prognostic models (adopt from [160]).
Figure 15
Figure 15
Data-driven and physics-based methods fusion prognostics framework.
See this image and copyright information in PMC

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