Fatigue Testing and Structural Health Monitoring of Retrofitted Web Stiffeners on Steel Highway Bridges

2013 ◽  
Vol 2360 (1) ◽  
pp. 27-35 ◽  
Author(s):  
Kasra Ghahremani ◽  
Ayan Sadhu ◽  
Scott Walbridge ◽  
Sriram Narasimhan
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Highway Bridges ◽  
Structural Health ◽  
Web Stiffeners ◽  
Steel Highway Bridges

Hybrid structural health monitoring for in-service highway bridges using wireless multiscale sensors

2012 ◽  
Author(s):  
Shinae Jang ◽  
Sushil Dahal ◽  
Gustavo K. Contreras ◽  
Jonathan Fitch ◽  
Jonathan Karamavros ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Highway Bridges ◽  
Structural Health

scholarly journals Structural Health Monitoring SHM of Highway bridges using Wireless Sensor Network

2018 ◽  
Vol Volume-2 (Issue-4) ◽  
pp. 1216-1221
Author(s):  
Pooja Bhadane ◽  
Akanksha Mali ◽  
Mukta Fulse | Jayashri Patil | Prof. S. B. Wagh ◽  
Keyword(s):  
Wireless Sensor Network ◽  
Structural Health Monitoring ◽  
Sensor Network ◽  
Health Monitoring ◽  
Highway Bridges ◽  
Wireless Sensor ◽  
Structural Health

An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades

2020 ◽  
Vol 19 (6) ◽  
pp. 1711-1725 ◽  
Author(s):  
Jaclyn Solimine ◽  
Christopher Niezrecki ◽  
Murat Inalpolat
Keyword(s):  
Signal Processing ◽  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Turbine Blades ◽  
Wind Turbine Blade ◽  
Wind Turbine Blades ◽  
Structural Health

This article details the implementation of a novel passive structural health monitoring approach for damage detection in wind turbine blades using airborne sound. The approach utilizes blade-internal microphones to detect trends, shifts, or spikes in the sound pressure level of the blade cavity using a limited network of internally distributed airborne acoustic sensors, naturally occurring passive system excitation, and periodic measurement windows. A test campaign was performed on a utility-scale wind turbine blade undergoing fatigue testing to demonstrate the ability of the method for structural health monitoring applications. The preliminary audio signal processing steps used in the study, which were heavily influenced by those methods commonly utilized in speech-processing applications, are discussed in detail. Principal component analysis and K-means clustering are applied to the feature-space representation of the data set to identify any outliers (synonymous with deviations from the normal operation of the wind turbine blade) in the measurements. The performance of the system is evaluated based on its ability to detect those structural events in the blade that are identified by making manual observations of the measurements. The signal processing methods proposed within the article are shown to be successful in detecting structural and acoustic aberrations experienced by a full-scale wind turbine blade undergoing fatigue testing. Following the assessment of the data, recommendations are given to address the future development of the approach in terms of physical limitations, signal processing techniques, and machine learning options.

scholarly journals Inspection and monitoring of wind turbine blade-embedded wave defects during fatigue testing

2014 ◽  
Vol 13 (6) ◽  
pp. 629-643 ◽  
Author(s):  
Christopher Niezrecki ◽  
Peter Avitabile ◽  
Julie Chen ◽  
James Sherwood ◽  
Troy Lundstrom ◽  
...  
Keyword(s):  
Structural Health Monitoring ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Health Monitoring ◽  
Fatigue Testing ◽  
Load Level ◽  
Wind Turbine Blade ◽  
Image Correlation ◽  
Strain Sensing ◽  
Structural Health

The research presented in this article focuses on a 9-m CX-100 wind turbine blade, designed by a team led by Sandia National Laboratories and manufactured by TPI Composites Inc. The key difference between the 9-m blade and baseline CX-100 blades is that this blade contains fabric wave defects of controlled geometry inserted at specified locations along the blade length. The defect blade was tested at the National Wind Technology Center at the National Renewable Energy Laboratory using a schedule of cycles at increasing load level until failure was detected. Researchers used digital image correlation, shearography, acoustic emission, fiber-optic strain sensing, thermal imaging, and piezoelectric sensing as structural health monitoring techniques. This article provides a comparison of the sensing results of these different structural health monitoring approaches to detect the defects and track the resultant damage from the initial fatigue cycle to final failure.

Sign in / Sign up

Export Citation Format

Share Document