Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: A new full-scale dataset for structural health monitoring

• URL: http://arxiv.org/abs/2406.04943v1
• Date: Fri, 7 Jun 2024 13:53:24 GMT
• Title: Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: A new full-scale dataset for structural health monitoring
• Abstract summary: This paper presents a new benchmark dataset capturing the dynamic response of a decommissioned BAE Systems Hawk T1A. It provides a key stepping stone between simple laboratory test structures and in-service structures. Using the dataset, non-linearity in the structure is demonstrated, as well as the sensitivity of the structure to damage of different types.
• Score: 0.32326259807823027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to prominence over recent decades and promise significant benefits when implemented in practice. However, significant challenges remain in the development of these methods. The introduction of realistic, full-scale datasets will be an important contribution to overcoming these challenges. This paper presents a new benchmark dataset capturing the dynamic response of a decommissioned BAE Systems Hawk T1A. The dataset reflects the behaviour of a complex structure with a history of service that can still be tested in controlled laboratory conditions, using a variety of known loading and damage simulation conditions. As such, it provides a key stepping stone between simple laboratory test structures and in-service structures. In this paper, the Hawk structure is described in detail, alongside a comprehensive summary of the experimental work undertaken. Following this, key descriptive highlights of the dataset are presented, before a discussion of the research challenges that the data present. Using the dataset, non-linearity in the structure is demonstrated, as well as the sensitivity of the structure to damage of different types. The dataset is highly applicable to many academic enquiries and additional analysis techniques which will enable further advancement of vibration-based engineering techniques.

Related papers

• Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines [14.989533826340486]
  We present a systematic, data-centric analysis of VLA research organized around three pillars: datasets, benchmarks, and data engines.<n>For datasets, we categorize real-world and synthetic corpora along embodiment diversity, modality composition, and action space formulation.<n>For benchmarks, we analyze task complexity and environment structure jointly, exposing structural gaps in compositional generalization.<n>For data engines, we examine simulation-based, video-reconstruction, and automated task-generation paradigms.
  arXiv  Detail & Related papers  (2026-04-24T20:41:59Z)
• From Parameters to Performance: A Data-Driven Study on LLM Structure and Development [73.67759647072519]
  Large language models (LLMs) have achieved remarkable success across various domains.<n>Despite the rapid growth in model scale and capability, systematic, data-driven research on how structural configurations affect performance remains scarce.<n>We present a large-scale dataset encompassing diverse open-source LLM structures and their performance across multiple benchmarks.
  arXiv  Detail & Related papers  (2025-09-14T12:20:39Z)
• Towards a Universal Vibration Analysis Dataset: A Framework for Transfer Learning in Predictive Maintenance and Structural Health Monitoring [0.36136619420474764]
  This research will include a broader range of vibration signals from multiple types of machinery. By mirroring the success of ImageNet in visual computing, this dataset has the potential to improve the development of intelligent systems in industrial applications.
  arXiv  Detail & Related papers  (2025-04-15T19:57:26Z)
• TopoFR: A Closer Look at Topology Alignment on Face Recognition [42.936929062768826]
  We propose TopoFR, a novel FR model that leverages a topological structure alignment strategy called PTSA and a hard sample mining strategy named SDE. PTSA uses persistent homology to align the topological structures of the input and latent spaces, effectively preserving the structure information and improving the generalization performance of FR model. Experimental results on popular face benchmarks demonstrate the superiority of our TopoFR over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-14T14:58:30Z)
• Fast and Reliable Probabilistic Reflectometry Inversion with Prior-Amortized Neural Posterior Estimation [73.81105275628751]
  Finding all structures compatible with reflectometry data is computationally prohibitive for standard algorithms. We address this lack of reliability with a probabilistic deep learning method that identifies all realistic structures in seconds. Our method, Prior-Amortized Neural Posterior Estimation (PANPE), combines simulation-based inference with novel adaptive priors.
  arXiv  Detail & Related papers  (2024-07-26T10:29:16Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets [110.60233593474796]
  DA-VEGAN is a model with two central innovations. A $beta$-variational autoencoder is incorporated into a hybrid GAN architecture. A custom differentiable data augmentation scheme is developed specifically for this architecture.
  arXiv  Detail & Related papers  (2023-02-17T08:49:09Z)
• Trust Your $\nabla$: Gradient-based Intervention Targeting for Causal Discovery [49.084423861263524]
  In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT. GIT 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines.
  arXiv  Detail & Related papers  (2022-11-24T17:04:45Z)
• Dataset: Impact Events for Structural Health Monitoring of a Plastic Thin Plate [0.0]
  We present a novel time-series dataset for impact detection and localization on a plastic thin-plate. The dataset was collected from an experimental procedure of low-velocity, low-energy impact events. The input measurements come from 4 PZT sensors placed at the corners of the plate.
  arXiv  Detail & Related papers  (2022-09-20T21:58:42Z)
• Controllable Data Generation by Deep Learning: A Review [22.582082771890974]
  controllable deep data generation is a promising research area, commonly known as controllable deep data generation. This article introduces exciting applications of controllable deep data generation, experimentally analyzes and compares existing works.
  arXiv  Detail & Related papers  (2022-07-19T20:44:42Z)
• CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection [0.618778092044887]
  Currently used AI-based data-driven methods for damage diagnostics and prognostics are centered on historical data of the structures. In this study, a variant of Generative Adversarial Networks (GAN), Cycle-Consistent Wasserstein Deep Convolutional GAN with Gradient Penalty (CycleWDCGAN-GP) model is used. The outcomes of this study demonstrate that the proposed model can accurately generate the possible future responses of a structure for potential future damaged conditions.
  arXiv  Detail & Related papers  (2022-02-16T02:31:38Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery [1.0036312061637764]
  Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure-property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is scarcely populated and of dubious quality. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure-property relationships from the literature.
  arXiv  Detail & Related papers  (2021-11-02T21:43:58Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.