Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning

Candon, Michael; Esposito, Marco; Fayek, Haytham M.; Levinski, Oleg; Koschel, Stephan; Joseph, Nish; Carrese, Robert; Marzocca, Pier

doi:10.1016/j.ymssp.2022.108809

Cited by 31 publications

(6 citation statements)

References 36 publications

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“…Quality management in assembly process 2022 [117] Information management 2022 [118] Geometrical variation prediction 2022 [119] Multi-input loads monitoring 2022 [120] AR-based Learning Speed and Task Performance. 2021 [121] Multi-dimensional machining process data 2021 [122] Condition Based Maintenance 2021 [123] Modelling simulations 2020 [103] Industry and Manufacturing Model-driven engineering 2021 [124] DTs and CPS 2019 [125] Production control optimization 2019 [126] Real-time feedback from virtual to real space 2021 [51] Planning and commissioning optimization 2021 [127] Smart Manufacturing System early detection 2021 [108] Energy DT-based energy optimization solutions 2019 [128] Distributed real-time process data 2017 [129] Performance predictions 2020 [130] Continuous tracking and simulation 2020 [131] Hybrid modelling performance monitoring method 2020 [114]…”

Section: Aerospacementioning

confidence: 99%

Digital Twin Framework for Built Environment: A Review of Key Enablers

Piras,

Agostinelli,

Muzi

2024

Energies

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The emergence of Digital Twin (DT) technology presents unique opportunities for society by facilitating real-time data transfer from the physical environment to its digital counterpart. Although progress has been made in various industry sectors such as aerospace, the Architecture, Engineering, Construction, and Operation (AECO) sector still requires further advancements, like the adoption of these technologies over traditional approaches. The use of these technologies should become standard practice rather than an advanced operation. This paper aims to address the existing gap by presenting a comprehensive framework that integrates technologies and concepts derived from purpose-driven case studies and research studies across different industries. The framework is designed to provide best practices for the AECO sector. Moreover, it aims to underscores the potential of DT for optimization through overseeing and digital management of the built environment across the entire life cycle of facilities, encompassing design, construction, operation, and maintenance. It is based on an extensive literature review and presents a holistic approach to outlining the roles of Building Information Modelling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and other key enablers within the DT environment. These digital tools facilitating the simultaneous evaluation of associated benefits, such as resource savings and future prospects, like monitoring project sustainability objectives.

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Section: Aerospacementioning

confidence: 99%

Digital Twin Framework for Built Environment: A Review of Key Enablers

Piras,

Agostinelli,

Muzi

2024

Energies

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“…With the development of The Times and the continuous progress of science and technology, the research on the aircraft operation security is becoming more and more attention [1].Generally, aircraft operate in complex environments and are subjected to complex dynamic loads, such as cyclic dynamic loads from the engine rotor and aerodynamic loads from the air. This is an issue as knowing the input loads of a mechanical structure is essential for structural design, fault diagnosis, health monitoring,and so on.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Load Identification at Natural Frequencies for Aircraft via Attention Based 1D-CNN

He,

Li,

Feng

et al. 2024

J. Phys.: Conf. Ser.

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Since the frequency response function is ill-conditioned at the natural frequency of the system, the traditional load identification method based on the system parameters is no longer applicable. Aiming at the difficulty of dynamic load identification at the natural frequency of the structural system, a dynamic load identification method at the natural frequency of the structure based on one-dimensional convolutional neural network(1D-CNN) with attention mechanism is proposed. Specifically, the high-level features in the vibration response signal are first extracted through the convolution layer. Then the weight matrix of the network is updated by backpropagation algorithm, which represents the importance of different features. The mapping relationship between response and load is established to realize the task of load identification. From the trained data, the attention module learns the contribution of features according to the different contribution of different features to load prediction. The important components in the response signal are highlighted and noise pollution is suppressed. Excitation and response signals at the natural frequency of the system were acquired using exciters and an accelerometer mounted on the GARTEUR aircraft model. Excitation and response signals at the natural frequencies of the system are obtained by an exciter and accelerometer mounted on the GARTEUR aircraft model. The responses of the model at the first three natural frequencies of 6.4Hz,35.8Hz and 48.5Hz were obtained respectively. Experimental results show that compared with the traditional TSVD load identification method, the maximum error of this method is only 3.19%. Compared with the 1D-CNN method, the proposed method has stronger robustness under 20%, 50% and 80% noise levels.

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“…Load monitoring of aircraft structures is a prerequisite. It can be realized based on flight parameter monitoring systems and strain gauges in critical parts, incorporating techniques such as finite element analysis, multiple linear regression, and artificial neural networks [8][9][10][11]. This lays a solid foundation for individual aircraft life monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Novel Individual Aircraft Life Monitoring Method Based on Reliable Life Consumption Assessment

Fu,

2023

Machines

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Individual life monitoring is crucial for ensuring aircraft flight safety. Conventional life-consumption-based monitoring methods ignore reliability, thus disjoining them from the aircraft’s reliable life determination and extension, where high confidence and reliability are required. Therefore, this paper proposes a reliable life consumption and individual life monitoring method for aircraft structure fatigue. In the paper, the P-S-N curve, i.e., the relationship between the aircraft structure’s life (N) and fatigue load (S) under a certain probability (P), is established, by which the lower confidence limit of the aircraft structure’s reliable life can be evaluated under any fatigue loads. Based on that and the aircraft’s monitored fatigue loads, the indexes of reliable life consumption and remaining reliable life percentages are proposed and assessed in real time for individual aircraft life monitoring and online life management. Case studies indicate that the proposed method can guarantee high confidence and reliability requirements in individual life monitoring, consistent with the aircraft’s life determination and extension, which are widely accepted nowadays in engineering practice.

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Advanced multi-input system identification for next generation aircraft loads monitoring using linear regression, neural networks and deep learning

Cited by 31 publications

References 36 publications

Digital Twin Framework for Built Environment: A Review of Key Enablers

Digital Twin Framework for Built Environment: A Review of Key Enablers

Dynamic Load Identification at Natural Frequencies for Aircraft via Attention Based 1D-CNN

A Novel Individual Aircraft Life Monitoring Method Based on Reliable Life Consumption Assessment

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