Introduction to Service of an Artificial Neural Network Based Fatigue Monitoring System

Reed, Steve; McCoubrey, Brian; Mountfort, Andy

doi:10.1007/978-90-481-2746-7_60

Cited by 3 publications

(1 citation statement)

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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%

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%