Statistical time series methods for damage diagnosis in a scale aircraft skeleton structure: loosened bolts damage scenarios
- 1. Stochastic Mechanical Systems and Automation (SMSA) Laboratory Department of Mechanical and Aeronautical Engineering University of Patras, GR 265 00 Patras (Greece)
Description
A comparative assessment of several vibration based statistical time series methods for Structural Health Monitoring (SHM) is presented via their application to a scale aircraft skeleton laboratory structure. A brief overview of the methods, which are either scalar or vector type, non-parametric or parametric, and pertain to either the response-only or excitation-response cases, is provided. Damage diagnosis, including both the detection and identification subproblems, is tackled via scalar or vector vibration signals. The methods' effectiveness is assessed via repeated experiments under various damage scenarios, with each scenario corresponding to the loosening of one or more selected bolts. The results of the study confirm the 'global' damage detection capability and effectiveness of statistical time series methods for SHM.
Availability note (English)
Available from http://dx.doi.org/10.1088/1742-6596/305/1/012056Additional details
Identifiers
Publishing Information
- Journal Title
- Journal of Physics. Conference Series (Online)
- Journal Volume
- 305
- Journal Issue
- 1
- Journal Page Range
- [10 p.]
- ISSN
- 1742-6596
Conference
- Title
- 9. international conference on damage assessment of structures
- Acronym
- DAMAS 2011
- Dates
- 11-13 Jul 2011
- Place
- Oxford (United Kingdom)
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 43071631
- Subject category
- S42: ENGINEERING;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- AIRCRAFT; BOLTED JOINTS; DAMAGE; DETECTION; ENGINEERING; FASTENERS; MECHANICAL STRUCTURES; MECHANICAL VIBRATIONS; MONITORING; SCALARS; VECTORS
- Descriptors DEC
- JOINTS; TENSORS