Damage detection using large sensor networks

ContactArnaud Deraemaeker
  
KeywordsStructural Health Monitoring, damage detection, modal filtering, large sensor networks
  
Current CollaborationsProf K. Worden, University of Sheffield, UK Dr Ch. Farrar, Los Alamos National Labs, United States
  
Past CollaborationsProf J. Kullaa, Helsinki Univeristy of Technology, Finland Dr L. Mevel and Prof. M. Basseville, INRIA/IRISA Dr B. Peeters, LMS International, Belgium Prof W. Ostachowicz, IFFM Gdansk, Poland Prof S. Chesné, INSA-Lyon, France Prof G. De Roeck and Prof G. Lombaert, KULeuven, Belgium
  
Structural health monitoringMany countries are facing serious security problems due to the aging of their civil engineering infrastructures. Inspections are usually visual and/or local, costly and may require stopping the traffic. For more than 20 years, many researchers have developed alternative global methods based on vibration measurements. The present trend is to use ambient vibrations (due to traffic, wind, …) in order to detect, locate and quantify the level of damage in the structure. Recently, important advances have been made in the field of instrumentation which makes it possible to equip structures with very large networks of sensors measuring many different physical quantities.   

SHM Millau s
Figure 1 : Civil infrastructure under ambient vibrations : The Millau viaduct
    
From Data to information The challenge is then to transform the raw data to meaningful information for structural health assessment. This step is referred to as “feature extraction” and requires advanced signal processing techniques. At ULB-BATir, we have been working on a novel feature extraction procedure based on modal filtering techniques [1,2]. Coupled to an automated alarm triggering technique based on control charts, the technique allows to perform on-line and remote damage detection on structures under ambient vibrations without any user interaction. The robustness of the technique against environmental changes can be achieve using statistical tools such as factor analysis as demonstrated in [3]. The technique has been successfully applied for the detection of different levels of damage induced on an aircraft wing in an experimental setup developed at the University of Sheffield by the team of Prof K. Worden (Figure 2).

SHM wing s Figure 2: Damage detection on an aircraft wing: laboratory experiment

The challenge is then to transform the raw data to meaningful information for structural health assessment. This step is referred to as “feature extraction” and requires advanced signal processing techniques. At ULB-BATir, we have been working on a novel feature extraction procedure based on modal filtering techniques [1,2]. Coupled to an automated alarm triggering technique based on control charts, the technique allows to perform on-line and remote damage detection on structures under ambient vibrations without any user interaction. The robustness of the technique against environmental changes can be achieve using statistical tools such as factor analysis as demonstrated in [3]. The technique has been successfully applied for the detection of different levels of damage induced on an aircraft wing in an experimental setup developed at the University of Sheffield by the team of Prof K. Worden (Figure 2).

SHM wing s Figure 2: Damage detection on an aircraft wing: laboratory experiment
  
Sensors for Structural health monitoringDeveloping efficient signal processing strategies is only meaningful if adequate sensing technologies are available and if their limitations are fully understood. In parallel to the numerical and analytical developments, we are developing experimental capabilities for dynamic measurements. Different types of sensors are available in our lab: conventional and seismic accelerometers, strain gauges, fiber optics FBGS strain sensors (coupled to a dynamic interrogation unit) and piezoelectric sensors. A wide variety of piezoelectric elements are used, from bulk PZT to piezocomposites (such as Macro Fiber Composite transducers) and PVDF films. A better understanding of their behavior is achieved through advanced computational techniques developed in parallel.

SHM sensors s
Figure 3: Sensors and actuators for Structural Health Monitoring
  
Support2006-2009: ESF-Eurocores-S3T : Smart Sensing for Structural Health Monitoring (S3HM)
    
Selected publications[1] A. Deraemaeker and A. Preumont. Vibration based damage detection using large array sensors and spatial filters. Mechanical Systems and Signal Processing, 20:1615–1630, 2006 [2] A. Deraemaeker. Vibration based structural health monitoring using large sensor arrays: overview of instrumentation and feature extraction based on modal filters. CISM Lecture Notes Vol 520. Springer, 2010 [3] A. Deraemaeker, E. Reynders, G. De Roeck, and J. Kullaa. Vibration-based structural health monitoring using output-only measurements under changing environment. Mechanical Systems and Signal Processing, 22:34–56, 2008