1,721,071 research outputs found
Uncertainty in structural dynamics
No full textThe effects of uncertainty are of growing concern in the design of engineering structures. The fact that the properties of the structure are uncertain implies that there is consequent uncertainty in the dynamic response. Similarly, there is inevitable manufacturing variability: mass-produced items are never identical. Indeed the properties of an individual system will change with time due to environmental conditions, loads, wear, etc.Uncertainty and variability raise issues concerning safety, reliability, quality of performance, worst-case behaviour and so on, and in turn these issues lead to demands for modelling methods which specifically include uncertainties in the properties of the structure. In the past, factors of safety might be introduced. However, the desire for greater efficiency, improved performance and reduced costs has led to a demand for improved computational methods, especially for high-cost structures. The goal is to apply such methods at the design stage to produce structures which are safe, reliable and have acceptable noise and vibration performance under all environmental and operating conditions which they are expected to encounter, and to produce designs which are robust with respect to manufacturing variability
Uncertainty Bounds on Higher-Order FRFs from Gaussian Process NARX Models
Full text linkOne of the most versatile and powerful algorithms for the identification of nonlinear dynamical systems is the NARMAX (Nonlinear Auto-regressive Moving Average with eXogenous inputs) approach. The model represents the current output of a system by a nonlinear regression on past inputs and outputs and can also incorporate a nonlinear noise model in the most general case. In recent papers, one of the authors introduced a NARX (no noise model) formulation based on Gaussian Process (GP) regression and derived the corresponding expressions for Higher-order Frequency Response Functions (HFRFs). This paper extends the theory for the GP-NARX framework by providing a means of converting the GP prediction bounds in the time domain into bounds on the HFRFs. The approach is demonstrated on the Duffing oscillator
Development of an autonomous continuous monitoring system for mechanical damage detection
No full textOn the use of the inverse finite element method to enhance knowledge sharing in population-based structural health monitoring
Full text linkEfficient Structural Health Monitoring (SHM) is critical for ensuring safety and improving the operation and maintenance of aerospace structures. This study focusses on advanced shape-sensing methods, such as the inverse Finite Element Method (iFEM), which can estimate the complete displacement field of a structure based on a restricted number of strain measurements, fostering continuous and real-time monitoring. This approach additionally provides valuable insights into the dynamic behaviour of a structure by extracting its Frequency Response Functions (FRFs) and modal properties to perform vibration-based SHM. However, effectively extending SHM to a fleet or population of structures would require a significant amount of data for each one, which may be unavailable or incomplete. A population-based Structural Health Monitoring (PBSHM) strategy can solve data scarcity by sharing knowledge between similar structures via transfer-learning algorithms. In PBSHM, handling data from diverse sources is paramount for achieving accurate results. Therefore, this study integrates iFEM into the PBSHM framework, enhancing knowledge transfer by harmonising fibre-optic strain measurements to vibration-based features and providing reliable source data to inform diagnostics on similar structures. The proposed approach is validated on a population of laboratory-scale steel aircraft subjected to specific operating and damage conditions tested using three different sensor setups
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