Bimetallic structures are used in a wide range of applications and play a key role in equipment such as aerospace and thermostats. However, the abundance of internal and external excitations and nonlinearities in bimetallic structures, as well as the complexity and variability of their operating environments, make their dynamics more complex to analyse compared to other systems. In addition, the interactions between the components that make up the bimetallic structure, wear and tear, and the operating environment will lead to uncertainties in the internal and external excitations and system parameters of the bimetallic structure. These uncertainties need to be taken into account in the dynamic analysis of bimetallic structures. At present, extensive research work has been carried out by scholars for the uncertainty analysis of bimetallic structures. We systematically review the current research status of the uncertainty dynamics of bimetallic structures by scholars at home and abroad in terms of the uncertainty in dynamics, beam dynamics, bolted joint dynamics, etc., and give the problems that need to be further investigated.
For certain structure types and damage sizes, guided waves offer some distinct advantages for damage detection, such as distance and sizing potential, greater sensitivity and cost effectiveness. Guided waves exhibit in multiple modes, of which for Lamb waves there are two shapes; symmetric and antisymmetric. In damage detection regimes, information and features of individual modes, which propagate from a single source, are useful for localisation and sizing of damage. This leads to motivation to decompose a single signal into the individual modes that are received in the wave-packet. Decomposition of wave modes is possible in full-field Lamb wave data through a forward-backward, two-dimensional Fourier transform method that involves dispersion curve information; though this method cannot be applied directly to signals at a single location. By using this method, the expected nominal waves can be determined for a given propagation distance; i.e. the individual wave modes expected to be present regardless of damage. In the presence of damage, residual signals will be present which contains information on the damage. In this paper, a Bayesian linear regression technique is used to decompose single multi-mode signals into their individual wave modes, which is then used to determine any residual signals. This decomposition is done by determining the expected shape and size of individual mode signals from the full-field decomposed waves. The information inferred by this method both before and after the wave has propagated through damage is studied.
Within structural health monitoring, the capture and use of acoustic emission is a popular technique for the localisation of damage. In particular, approaches that view localisation as a problem of spatial mapping have performed well when applied to structures containing inhomogeneities such as complex geometrical features or the composition of multiple materials. The maps first require a series of artificial acoustic emission events to be generated across a test specimen, resulting in a mapping that represents difference-in-time-of arrival (dTOA) information. Despite their success, the application of dTOA maps has generally been restricted to applications that can be characterised by Euclidean distance measures. For spherical geometries such as a bearing raceway, this geometrical definition is not representative of the domain. This paper therefore proposes a novel extension to the spatial mapping approach for spherical domains, allowing acoustic emission localisation on a spherical roller bearing. The presented methodology firstly poses the generation of dTOA mappings as a problem of Gaussian process regression. Through a Bayesian framework, the source location likelihood of a real acoustic emission event is then assessed across the surface of the structure. Under the standard Gaussian process convention, the assumption is made that inputs to the kernel can be represented as a function of Euclidean distance. However, as bearings exist in a spherical space, the Euclidean distance will ignore the topological constraints of the bearing and is therefore not an appropriate measure. To bypass this issue, a reduced-rank approach is taken that expresses the covariance function as an approximate eigendecomposition. This approach allows the inputs to be projected onto an eigenbasis, satisfying both the conditions of a valid covariance function, as well as the topological constraints imposed by the geometry of the bearing. The proposed method is then applied to localise AE on a spherical roller bearing that is designed to replicate planetary support bearings that are found in wind turbine gearboxes.
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