Toogle Left

BATir banner


logo SMC logo SMC  DYNA logo SMC  DYNA  SHM DD Damage detection using large sensor networks

Arnaud Deraemaeker


Structural Health Monitoring, damage detection, modal filtering, large sensor networks

Current Collaborations

Prof K. Worden, University of Sheffield, UK

Dr Ch. Farrar, Los Alamos National Labs, United States


Past Collaborations

Prof 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


Many 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


Sensors for Structural health monitoring

Developing 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


2006-2009: 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