Structural Damage Identification

1. Structural Damage Identification LOW FREQUENCY TECHNIQUES Patrick Guillaume

2. Damage Detection Philosophy Rytter (1993*,**) introduced a damage state classification system which has been widely accepted by the community dealing with damage detection and SHM. Following these lines, the damage state is described by answering the following questions (Sohn et al. (2003)): (1) Is there damage in the system? (existence) (2) Where is the damage in the structure? (location) (3) What kind of damage is present? (type) (4) How severe is the damage? (extent) (5) How much useful life remains? (prognosis)

3. The Monitoring Process (1/2) • 1. Operational Evaluation Operational evaluation answers questions related to the damage detection system implementation, such as economic issues, possible failure modes, operational and environmental conditions and data acquisition related limitations. • 2. Data Acquisition, Fusion and Cleansing Data acquisition is concerned with the quantities to be measured, the type and quantity of sensors to be used, the locations where these sensors are to be placed, sensor resolution, bandwidth, and hardware. Data fusion, as a discipline of SHM, is the ability to integrate data acquired from the various sensors in the measurement chain. Data cleansing is the process of selecting significant data from the multitude of information, i.e., the determination of which data is necessary (or useful) in the feature selection process.

4. The Monitoring Process (2/2) • 3. Feature Extraction and Information Condensation Feature extraction is the process of identifying damage sensitive properties, which allow one to distinguish between the damaged and undamaged structural states. Information condensation becomes increasingly advantageous and necessary as the quantity of data increases, particularly if comparisons are to be made between sets of data obtained over the life cycle of a system. • 4. Statistical Model Development for Feature Discrimination An important issue in the development of statistical models is to establish the model features sensitivity to damage and to predict false damage identification.

5. Damage in Composite Materials • The use of fibre reinforced plastics (FRP) as an alternative • to conventional materials, such as metallic alloys, is undergoing • increasing growth, especially in the aeronautical, naval • and automotive industries, because of their excellent mechanical • properties, low density and easy of shaping. • Composite materials possess specific strengths and Young’s • moduli many times greater than those of the most widely used • metallic materials, such as steel, aluminum and titanium. • However, the extreme sensitivity of composite materials to • impact loads constitutes a hindrance to their utilization. In • aeronautical structures, for example, the components may have • to undergo (i) low energy impacts caused by dropped tools or • mishandling during assembly and maintenance, (ii) medium • energy impacts caused in-service by foreign objects such as • stones or birds and (iii) high energy impacts caused by military • projectiles (Matthews (1999), Silva (2001) and Carvalho • (2003)).

6. Some Damage Detection Techniques • Static Stiffness Variations Linear stiffness in function of the number of fatigue cycles Static stiffness variations during crack growth in a slat track A320

7. Some Damage Detection Techniques • Natural Frequencies and FRFs The development of modal analysis techniques for damage detection and SHM arose from the observation that changes in the structural properties have consequences on the natural frequencies. Nevertheless, the relatively low sensitivity of natural frequency to damage requires high levels of damage and measurements made with high accuracy in order to achieve reliable results. Moreover, the capacity to locate damage is somewhat limited, as natural frequencies are global parameters and modes can only be associated with local responses at high frequencies.

8. Some Damage Detection Techniques • Natural Frequencies and FRFs Messina et al. (1992) proposed the damage location assurance criterion (DLAC) in location j, which is a correlation similar to the modal assurance criterion (MAC) of Allemang and Brown (1982), and is given by: the experimental frequency shift vector the analytical frequency shift vector A zero value indicates no correlation and a unity value indicates perfect correlation between the vectors involved in the DLAC relationship. Damage location and dimension is identified by maximizing this objective function.

9. Some Damage Detection Techniques • Natural Frequencies and FRFs Zang et al. (2003a) presented criteria's to correlate measured frequency responses from multiple sensors and proposed using them as indicators for structural damage detection. One criterion is the global shape correlation (GSC) function, which is sensitive to mode shape differences but not to relative scales, being defined as: is a column of FRF baseline data is a column of the current measured FRF data

10. Some Damage Detection Techniques • Mode Shape Changes (MAC) The MAC value (Modal Assurance Criteria), which is probably the most common way of establishing a correlation between experimental and analytical models, is defined by Allemang and Brown (1982) as: West (1984*) uses the MAC to determine the level of correlation between modes from the test of an undamaged Space Shuttle Orbiter body flap and the modes from the test of the flap after loading.

11. Some Damage Detection Techniques • Mode Shape Changes (COMAC) Although the MAC can provide a good indication of the disparity between two sets of data, it does not show explicitly where in the structure is the source of discrepancy. The co-ordinate MAC (COMAC) has been developed from the original MAC. It is the reverse of the MAC in that it measures the correlation at each degree-of-freedom (DOF) averaged over the set of correlated mode pairs. The COMAC identifies the co-ordinates at which two sets of mode shapes do not agree, and is defined as (Lieven and Ewins (1988)):

12. Some Damage Detection Techniques • Mode Shape Curvature As an alternative to the use of mode shapes as damage features, mode shape curvature (i.e., second order derivative) can be used to obtain spatial information about vibration changes. It has been reported in literature that absolute changes in mode shape curvature can be a good indicator of structural damage (Pandey et al., 1991).

13. Some Damage Detection Techniques • Changes in Modal Flexibility Another class of damage identification methods makes use of the dynamically measured flexibility matrix to estimate changes in the static behaviour of the structure. Typically, damage is assessed by comparing the measured flexibility matrix, computed on a basis of the reference modal data, to the measured flexibility matrix computed on a basis of the damaged condition.

14. Some Damage Detection Techniques • Changes in Strain Energy Instead of using mode shape curvature directly, derived quantities, such as strain energy, can be chosen as damage features. The Changes in Strain Energy (CSE) method localizes structural damage as a decrease in modal strain energy between 2 structural DOFs (Stubbs et al., 1992). For a linear elastic beam structure, the strain energy can be computed on a basis of the mode shape curvature. In that case, the damage index for element i centered around DOF i, can be written as

15. Some Damage Detection Techniques • Sensitivity-based Approach The last method is based on the interpretation of changes in natural frequency by means of the sensitivity of the natural frequencies of a reference condition to local mass changes in an experimental DOF or local stiffness changes between two adjacent DOFs. The method requires both the natural frequencies and normalized mode shape estimates of the structure in its reference condition as well as the corresponding natural frequency estimates of the damaged condition.

16. Some Damage Detection Techniques • Sensitivity-based Approach • Combined approach

17. Some Damage Detection Techniques • Sensitivity-based Approach • Iterative Weighted Pseudo Inverse

18. Experimental Validation

19. Experimental Validation

20. Experimental Validation • MAC

21. Experimental Validation • COMAC

22. Experimental Validation • Mode Shape Curvature

23. Experimental Validation • Strain Energy

24. Experimental Validation • Modal Flexibility

25. Experimental Validation • Sensitivity-based Approach

26. Experimental Validation • Sensitivity-based Approach (combined approach)

27. Experimental Validation • Sensitivity-based Approach (combined approach) • Iterative Weighted Pseudo Inverse

28. Experimental Validation

29. Experimental Validation

30. Experimental Validation

31. Experimental Validation

32. Experimental Validation