Methodologies of Damage Identification Using Non-Linear Data-Driven Modelling

Methodologies of Damage Identification Using Non-Linear Data-Driven Modelling

Miguel Angel Torres Arredondo (MAN Diesel and Turbo SE, Germany), Diego Alexander Tibaduiza Burgos (Universidad Santo Tomás, Colombia), Inka Buethe (University of Siegen, Germany), Luis Eduardo Mujica (Department of Applied Mathematics, Universitat Politècnica de Catalunya, Spain), Maribel Anaya Vejar (Universidad Santo Tomás, Colombia & Universitat Politècnica de Catalunya, Spain), Jose Rodellar (Department of Applied Mathematics, Universitat Politècnica de Catalunya, Spain) and Claus-Peter Fritzen (University of Siegen, Germany)
Copyright: © 2015 |Pages: 14
DOI: 10.4018/978-1-4666-5888-2.ch093
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Theoretical Background

Structural health monitoring (SHM) can be defined as the process of implementing a damage identification strategy for a variety of infrastructures (Farrar & Worden, 2007). The main difference between SHM and non-destructive testing (NDT) techniques is that sensors are permanently installed on the structure providing continuous or on-demand measurements. According to (Rytter, 1993), SHM can be considered as a four-levels process which are defined in hierarchical order as follows:

  • Detection: Provides a qualitative indication of the presence of damage.

  • Localisation: Provides information about the position of the damage.

  • Assessment: Provides a quantitative indication of the extent of the damage.

  • Prediction: Provides an estimate of the residual life of the structure.

The advantages of using an SHM system are clearly an improved safety, reduction of inspection time, maintenance costs and structural down-time. There exist a number of techniques used for the identification of damage.

Key Terms in this Chapter

Wavelet Transform: Capable of revealing aspects of data that other signal analysis techniques miss, aspects like trends, breakdown points, discontinuities in higher derivatives, and self-similarity. It will provide accurate location of the transient signals while simultaneously reporting the fundamental frequency and its low-order harmonics.

Nonlinear Principal Component Analysis (NLPCA): Commonly seen as a nonlinear generalization of standard principal component analysis (PCA). It generalizes the principal components from straight lines to curves (nonlinear). Thus, the subspace in the original data space which is described by all nonlinear components is also curved.

Receiver Operating Characteristics (ROC) Curves: A tool for diagnostic test evaluation and they are well-known for describing the performance of diagnostic and detection systems in medical decision, signal processing and communications. These curves allow analyzing the balance between the false positive rate and the sensitivity for different cut-off points of a parameter.

Self-Organizing Maps (SOMs): Belong to the group of Artificial Neural Networks (ANN) and can be described as a nonlinear, ordered, smooth mapping of high-dimensional input data on the elements of a regular, low-dimensional display. They use an unsupervised algorithm and are also known as Kohonen Maps.

Structural Health Monitoring (SHM): The process of acquiring and analysing data from a set of permanently installed sensors to determine the health of a structure in a non-destructive way.

Acousto-Ultrasonics: A highly sophisticated and advanced technique using digital signal processing and pattern recognition algorithms. The method consists of monitoring and analyzing the acoustic signals received in order to find the presence of a discontinuity (delaminations, debonding, etc.) inside the tested structure.

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