Application of Rare Event Anticipation Techniques to Aircraft Health Management

Alestra, Stephane; Bordry, Cristophe; Brand, Cristophe; Burnaev, Evgeny; Erofeev, Pavel; Papanov, Artem; Silveira-Freixo, Cassiano

doi:10.4028/www.scientific.net/amr.1016.413

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…Predictive Maintenance (PM) is one way to reduce costs and downtimes by planning maintenance work based on an asset's actual condition rather than relying on fixed time-based maintenance cycles [2]. Multiple industries like aviation [3,4], manufacturing [5,6], and chemistry [7,8] vastly apply PM into their operational routine. Also, other industries like construction and facility management can benefit from improved maintenance methodologies.…”

Section: Introductionmentioning

confidence: 99%

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

Lowin

2024

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Introduction: Due to the lack of labeled data, applying predictive maintenance algorithms for facility management is cumbersome. Most companies are unwilling to share data or do not have time for annotation. In addition, most available facility management data are text data. Thus, there is a need for an unsupervised predictive maintenance algorithm that is capable of handling textual data. Methodology: This paper proposes applying association rule mining on maintenance requests to identify upcoming needs in facility management. By coupling temporal association rule mining with the concept of semantic similarity derived from large language models, the proposed methodology can discover meaningful knowledge in the form of rules suitable for decision-making. Results: Relying on the large German language models works best for the presented case study. Introducing a temporal lift filter allows for reducing the created rules to the most important ones. Conclusions: Only a few maintenance requests are sufficient to mine association rules that show links between different infrastructural failures. Due to the unsupervised manner of the proposed algorithm, domain experts need to evaluate the relevance of the specific rules. Nevertheless, the algorithm enables companies to efficiently utilize their data stored in databases to create interpretable rules supporting decision-making.

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Section: Introductionmentioning

confidence: 99%

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

Lowin

2024

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“…These conditions are of a big interest because many real-world data analysis problems have inherent peculiarities which lead to unavoidable imbalances in available datasets. Examples of such problems include network intrusion detection (Kruegel et al [1], Burnaev et al [2]), oil spill detection from satellite images (Kubat et al [3], Trekin et al [4]), detection of fraudulent transactions on credit cards (Chan and Stolfo [5]), diagnosis of rare diseases (Rahman and Davis [6]), prediction and localization of failures in technical systems (Tremblay et al [7], Alestra et al [8]), etc. These and many other examples have one common significant feature: target events (diseases, failures, etc.)…”

Section: Introductionmentioning

confidence: 99%

Meta-learning for resampling recommendation systems

Smolyakov

Korotin

Erofeev

et al. 2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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One possible approach to tackle the class imbalance in classification tasks is to resample a training dataset, i.e., to drop some of its elements or to synthesize new ones. There exist several widely-used resampling methods. Recent research showed that the choice of resampling method significantly affects the quality of classification, which raises the resampling selection problem. Exhaustive search for optimal resampling is time-consuming and hence it is of limited use. In this paper, we describe an alternative approach to the resampling selection. We follow the meta-learning concept to build resampling recommendation systems, i.e., algorithms recommending resampling for datasets on the basis of their properties.

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“…CP detection problems span many applied areas and include automatic video surveillance based on motion features (Pham et al, 2014), intrusion detection in computer networks (Tartakovsky et al, 2006), anomaly detection in data transmission networks (Casas et al, 2010), anomaly detection for malicious activity (Burnaev et al, 2015a,b;Burnaev and Smolyakov, 2016), change-point detection in software-intensive systems (Artemov et al, 2016;Artemov and Burnaev, 2016a,b), fault detection in vehicle control systems (Malladi and Speyer, 1999;Alestra et al, 2014), detection of onset of an epidemic (MacNeill and Mao, 1995), drinking water monitoring (Guépié et al, 2012) and many others.…”

Section: Introductionmentioning

confidence: 99%

Inductive Conformal Martingales for Change-Point Detection

Volkhonskiy,

Nouretdinov,

Gammerman

et al. 2017

Preprint

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We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we propose a new method for change-point detection based on Inductive Conformal Martingales, which requires only the independence and identical distribution of observations. We compare the proposed approach to standard methods, as well as to change-point detection oracles, which model a typical practical situation when we have only imprecise (albeit parametric) information about pre-and post-change data distributions. Results of comparison provide evidence that change-point detection based on Inductive Conformal Martingales is an efficient tool, capable to work under quite general conditions unlike traditional approaches.

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Application of Rare Event Anticipation Techniques to Aircraft Health Management

Cited by 8 publications

References 3 publications

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

Meta-learning for resampling recommendation systems

Inductive Conformal Martingales for Change-Point Detection

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