Indirect aircraft structural monitoring using artificial neural networks

Reed, Steve

doi:10.1017/s0001924000002190

Cited by 11 publications

(7 citation statements)

References 7 publications

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“…It is concluded that this technology could provide the basis for accurate, costeffective structural usage monitoring systems. Consequently in [18] and [19] a structural health and usage neural network (SHAUNN) monitoring system is proposed to predict stresses, strains, loads, or fatigue damage from flight parameters.…”

Section: Methods Descriptionsmentioning

confidence: 99%

“…Other publications like [11,17,18] follow the same approach to model the loads at the vertical tail plane of an aircraft in dependency of aircraft system parameters and include additional input parameters such as the deflection of flaps and ailerons. Table 1 summarises the selected parameters used within this paper for each of the considered loads.To simplify, obtaining a mapping function describing the relation between the input and the output parameters basic physical laws are used [17] to combine the selected input parameters to reflect the relation present between the chosen input parameters and the considered output parameters.…”

Section: Parameter Selectionmentioning

confidence: 99%

“…MLPs with one and two hidden layers with a different number of neurons have been tested for comparison resulting in a total of 24 different architectures in 150 training sessions. In accordance with [9,18], a Levenberg-Marquardt algorithm is used for the training with a learning rate set to 0.1. The training is done with a maximum of 500 training epochs, using an early stopping criteria which leads to less epochs in the trained network.…”

Section: Application Of Multilayer Perceptronsmentioning

confidence: 99%

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Comparison of real-time flight loads estimation methods

2014

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Nowadays flight load exceedance monitoring is an important task to both: the aircraft manufacturer as well as the operator. The estimation of flight loads is required in several phases during development and operation of an aircraft. The requirements are usually different, for e.g. calculation of design loads for certification and operational loads monitoring of stress and fatigue. The ability to determine aircraft operational loads (more) precisely may reduce the time in maintenance. Being able to detect critical load exceedance events during flight or in a post-process is also an enabler for e.g. loads/fatigue monitoring at operator level. In this paper, a novel system identification method named local model networks is applied to the field of flight loads estimation and compared to approaches based on artificial neural networks as found in the literature. The presented approach tries to overcome some limitations with respect to model creation, robustness, interand extrapolation.

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Section: Methods Descriptionsmentioning

confidence: 99%

Section: Parameter Selectionmentioning

confidence: 99%

Section: Application Of Multilayer Perceptronsmentioning

confidence: 99%

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Comparison of real-time flight loads estimation methods

2014

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“…Therefore, health and usage monitoring of helicopters is an important research area (Wallace et al , 2004). Engineering health‐monitoring systems are often based on a mathematical model of the system, which is used to simulate the effect of damage on the system behavior (Reed, 2008; Trendafoilova et al , 2009; Ganguli et al , 1998). The development of a rotor helicopter health‐ and usage‐monitoring systems requires a mathematical model of the damaged rotor system.…”

Section: Helicopter Rotor Health Monitoringmentioning

confidence: 99%

Rotorcraft research in India: recent developments

Ganguli

2010

Aircraft Eng & Aerospace Tech

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to discuss published research in rotorcraft which has taken place in India during the last ten years. The helicopter research is divided into the following parts: health monitoring, smart rotor, design optimization, control, helicopter rotor dynamics, active control of structural response (ACSR) and helicopter design and development. Aspects of health monitoring and smart rotor are discussed in detail. Further work needed and areas for international collaboration are pointed out. Design/methodology/approach -The archival journal papers on helicopter engineering published from India are obtained from databases and are studied and discussed. The contribution of the basic research to the state-of-the-art in helicopter engineering science is brought out. Findings -It is found that strong research capabilities have developed in rotor system health and usage monitoring, rotor blade design optimization, ACSR, composite rotor blades and smart rotor development. Furthermore, rotorcraft modeling and analysis aspects are highly developed with considerable manpower available and being generated in these areas. Practical implications -Two helicopter projects leading to the "advanced light helicopter" and "light combat helicopter" have been completed by Hindustan Aeronautics Ltd These helicopter programs have benefited from the basic research and also provide platforms for further basic research and deeper industry academic collaborations. The development of well-trained helicopter engineers is also attractive for international helicopter design and manufacturing companies. The basic research done needs to be further developed for practical and commercial applications. Originality/value -This is the first comprehensive research on rotorcraft research in India, an important emerging market, manufacturing and sourcing destination for the industry.

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“…Azzam et al [16] also described the development of methods to predict damage in helicopter components and the prediction of high-frequency events, such as buffet loading on the fin of a fixed-wing aircraft. The work reported in the current paper was inspired by the work of Reed [18,19], who developed an ANN-based parametric fatigue monitor for the wing and tailplane of a military trainer aircraft and the wing of a combat aircraft.…”

Section: Introductionmentioning

confidence: 99%

Prediction of landing gear loads using machine learning techniques

Holmes

Sartor

Reed

et al. 2016

Structural Health Monitoring

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This paper investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, results on drop test data and flight test data will be examined. This paper will focus on the use of Gaussian Process regression for the prediction of loads on components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as the on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the GP to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear by using measurements that are readily available, or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements is considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction.

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Indirect aircraft structural monitoring using artificial neural networks

Cited by 11 publications

References 7 publications

Comparison of real-time flight loads estimation methods

Comparison of real-time flight loads estimation methods

Rotorcraft research in India: recent developments

Prediction of landing gear loads using machine learning techniques

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