Comparison of immunity-based schemes for aircraft failure detection and identification

Azzawi, Dia Al; Moncayo, Hever; Perhinschi, Mario G.; Perez, Andres; Togayev, Adil

doi:10.1016/j.engappai.2016.02.017

Cited by 9 publications

(3 citation statements)

References 44 publications

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“…For future work, because the failure has happened suddenly, the failure self-detection which is integrated into the controller design framework by employing the disturbance observer, 24 immunity-based schemes 25 should be considered. Furthermore, the failure detection and the corresponding safety control problem in case of low-altitude airdrop should be considered since the aircraft's attitude variation in low altitude is intolerable.…”

Section: Resultsmentioning

confidence: 99%

Cargo blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop

Chen

Song

2018

International Journal of Advanced Robotic Systems

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This article investigates the cargo's blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop. As the cargos move backward and drop out, the continuous variation of center of gravity for the whole system will deteriorate flight quality dramatically. Furthermore, due to various mechanical reasons, if the cargo is blocked on the delivery channel and the airdrop process is suspended suddenly at this time, the flight safety may be threatened. In view of this, the blocking failure is analyzed based on the aircraft model in this article and then the simulation is completed to show the failure's impact on aircraft's flight quality. Next, based on the uncertainty analysis and introduction of interval robust control theory, the safety controller is designed to stable the closed-loop system. The final simulation shows the proposed safety control strategy's effectiveness.

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Section: Resultsmentioning

confidence: 99%

Cargo blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop

Chen

Song

2018

International Journal of Advanced Robotic Systems

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“…AI and machine learning (ML) have been widely used in different engineering applications such as structural health monitoring (Al Azzawi et al, 2016;Liu et al, 2011;Saeidpour et al, 2018;Salehi and Burgueno, 2018;Salehi et al, 2018aSalehi et al, ,b, 2019aSantos et al, 2016;Silva et al, 2016;Wootton et al, 2017) and fatigue crack detection Rovinelli et al, 2018). ML algorithms, in the context of failure analysis, have been used for numerous applications, including phase-field models of polymer-based dielectrics (Shen et al, 2019), phase-field models of solidification (Yabansu et al, 2017), and crystal plasticity (Papanikolaou et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Failure Prediction in Brittle Materials: A Phase Field-Based Machine Learning Framework

Moraes

Salehi

Zayernouri

2021

J Mach Learn Model Comput

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Failure in brittle materials led by the evolution of micro-to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor node responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework and the applicability of artificial intelligence and ML to a practical engineering problem, i.e., data-driven failure prediction in brittle materials.

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“…AI and machine learning (ML) have been widely used in different engineering applications such as structural health monitoring [26,39,1,43,52,32,34,36,35,37,33] and fatigue crack detection [31,24,23]. ML algorithms, in the context of failure analysis, have been used for numerous applications, including phase-field models of polymer-based dielectrics [42], phase-field models of solidification [53], and crystal plasticity [30].…”

mentioning

confidence: 99%

Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework

Moraes¹,

Salehi²,

Zayernouri³

2020

Preprint

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Failure in brittle materials led by the evolution of micro-to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Timeseries data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor nodes responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework, and the applicability of artificial intelligence and ML to a practical engineering problem, i.,e, data-driven failure prediction in brittle materials.

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Comparison of immunity-based schemes for aircraft failure detection and identification

Cited by 9 publications

References 44 publications

Cargo blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop

Cargo blocking failure analysis, simulation, and safety control of transport aircraft with continuous heavy airdrop

Data-Driven Failure Prediction in Brittle Materials: A Phase Field-Based Machine Learning Framework

Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework

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