Clustering based background learning for hyperspectral anomaly detection

Aghili, Mohamad Ebrahim; Imani, Maryam; Ghassemian, Hassan

doi:10.1016/j.ejrs.2023.06.001

Cited by 7 publications

(1 citation statement)

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“…The algorithm generates k groups of data points that exhibit similarity. Anomalies may be identified for data instances that do not belong to these groupings [88].…”

Section: Clustering-based Anomalymentioning

confidence: 99%

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

Fu,

Avdelidis

2023

Sensors

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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.

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“…The algorithm generates k groups of data points that exhibit similarity. Anomalies may be identified for data instances that do not belong to these groupings [88].…”

Section: Clustering-based Anomalymentioning

confidence: 99%