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Damage Recognition for Structural HealthMonitoring

Yujie Ying, Joel Harley, and Jun ShiCarnegie Mellon University

Pittsburgh, PA 15213Email: yying@cmu.edu

Abstract—In the development of structural health monitoringsystems, creating robust detection schemes that are invariant toenvironmental and operation conditions has become a importantconcern for the community. In this proposal we provide somebackground as to why this is and discuss how we plan to usemachine learning techniques to develop such a robust system.Using experimental data and support vector machine methods,we plan to develop and system which can distinguish damagefrom pressure, temperature, and surface moisture.

I. INTRODUCTION AND BACKGROUND

The fields of nondestructive testing (NDT) and structuralhealth monitoring (SHM) focus on the design of systemsand technique capable of detecting damage in solid struc-tures [1]. Such structures include, among other things, pipes,bridges, buildings, airplanes, and ships. These tests can beaccomplished in many ways. Systems have been developedwhich, among other things, evaluate structures by means ofelectromagnetic [2], vibration [3], and ultrasonic [4] testing. Inthis proposal, we will focus on the use of ultrasonic techniques.

In traditional ultrasonic NDT, a sensor probe is movedmanually across a structure under test. These probes areusually designed to measure the material thickness or detectbackscatter from cracks in the material. Unfortunately, thisis not a valid solution for evaluating many critical structures.For example, the practical difficulties associated with manuallytesting a 100 mile pipe system or active space shuttle areeasy to identify. For such situations, the development of SHMsystems, which use sparse, permanently attached or embeddedsensors to continuously evaluate the structure, is preferable.

For our project, we will use a sparse set of ultrasonicsensors to generate guided waves inside of a stainless steelpipe. Ultrasonic guided waves are popular for structural healthmonitoring because they can travel large distances and theypropagate through the thickness of the specimen’s material [5].As a consequence, ultrasonic guided waves are very sensitiveto changes in the material caused by cracks, corrosion, orother forms of damage. Unfortunately, ultrasonic guided wavesare also sensitive to many other benign changes, such astemperature [6], pressure, and surface moisture [7]. This,coupled with the fact that ultrasonic guided wave propagationis extremely complex causes the interpretation of ultrasonicdata to be tenuous. An example ultrasonic signal can be shownin figure 3

Fig. 1. Stainless steel pipe specimen.

II. PROBLEM STATEMENT

In typically SHM damage detection techniques, the pristinecondition of a structure is recorded as a baseline (reference)signal during an early stage of a structure’s lifetime. The sig-nals collected of the structure’s current state are then comparedto that baseline data. The difference between these two mea-surements are considered to be damage. However, there arecertain disadvantages with relying on a baseline signal in realfield applications because operational and environmental vari-ations tend to distort the signals and masquerade as damage.This generates a significant number of false positive results.As an illustration, 2 show the difference signals caused bydamage (center) and temperature change (right) in a steel pipeafter baseline (left) subtraction. The two difference signalsare largely similar, and are therefore hard to distinguished.This problem as described above will be addressed in ourproject, i.e., how to recognize structural damage from harmlessenvironmental and operational variations.

Since the propagation of these waves and the effects on themby both benign and non-benign forces are difficult model, amachine learning approach seems practical. Our group would

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Fig. 2. (Left) Baseline ultrasonic signal. (Center) Change caused by damage. (Right) Change cause by temperature.

Fig. 3. Example measured ultrasonic guided wave signal after propagatingthrough a stainless steel pipe.

like to apply machine learning classification techniques inorder to distinguish damage from more benign effects ina stainless steel pipe, shown in figure 1, or aluminum barspecimen.

III. PROPOSED APPROACHES

We will divide this proposed work into three tasks: 1)to design and fulfill laboratory tests to acquire sufficientdatasets under different environmental conditions; 2) to extractappropriate features that will be sensitive to either damageor ambient variations; and 3) to develop efficient machinelearning algorithms that will properly demarcate the featurespaces.

For the first task, we will perform experiments on analuminum bar or a steel pipe specimen. Measurements will betaken under both undamaged and damaged conditions (a massscatterer will be used to simulate damage on the specimen),incorporating three factors of variability: temperature, surfacemoisture of the specimen, and internal pressure (for pipe).

For the second task, various features will be taken intoaccount and carefully evaluated. Signal processing techniqueswill be employed in order to extract features from both timedomain and frequency domain. Principal component analysis(PCA) will be used to determine the dominated features froma large number of possibly correlated features. Other signalfeatures that have proved useful, such as differential curvelength [7], may also be investigated.

For the third task, we will attempt Kernel methods (KMs)for our problem, such as support vector machines (SVMs).These methods have been empirically shown to be a veryeffective classifiers [8], [9]. We will form a binary clas-sification problem: damaged and undamaged (both undergoenvironmental changes). We will evaluate different Kernelfunctions for our specific problem and determine suitableparameters of the Kernel functions. If time permits, othermachine learning methods will also be exploited and comparedto SVM.

IV. EXPECTED RESULTS

Managing environmental and operation conditions on spec-imens under test is an important, open problem in the study ofstructural health monitoring. We hope to be show that machinelearning will help provide the tools and insight for creatingmore robust systems. Demonstration of effective machinelearning techniques would be useful and instructive to thecommunity as a whole.

REFERENCES

[1] K. Worden, C. R. Farrar, G. Manson, and G. Park, “The fundamentalaxioms of structural health monitoring,” Proceedings of the Royal SocietyA: Mathematical, Physical and Engineering Sciences, vol. 463, no. 2082,pp. 1639–1664, Jun. 2007.

[2] K. P. Chong, N. J. Carino, and G. Washer, “Health monitoring of civilinfrastructures,” Smart Materials and Structures, vol. 12, no. 3, pp. 483–493, Jun. 2003.

[3] H. Sohn, “Effects of environmental and operational variability on struc-tural health monitoring.” Philosophical transactions. Series A, Mathemat-ical, physical, and engineering sciences, vol. 365, no. 1851, pp. 539–60,Feb. 2007.

[4] D. N. Alleyne and P. Cawley, “The interaction of Lamb waves withdefects.” IEEE transactions on ultrasonics, ferroelectrics, and frequencycontrol, vol. 39, no. 3, pp. 381–97, Jan. 1992.

[5] P. Wilcox, M. Lowe, and P. Cawley, “Mode and Transducer Selectionfor Long Range Lamb Wave Inspection,” Journal of Intelligent MaterialSystems and Structures, vol. 12, no. 8, pp. 553–565, 2001.

[6] A. J. Croxford, J. Moll, P. D. Wilcox, and J. E. Michaels, “Efficienttemperature compensation strategies for guided wave structural healthmonitoring,” Ultrasonics, vol. 50, no. 4-5, pp. 517–28, Apr. 2010.

[7] Y. Lu, J. E. Michaels, and S. Member, “Feature Extraction and SensorFusion for Ultrasonic Structural Health Monitoring Under ChangingEnvironmental Conditions,” IEEE Sensors Journal, vol. 9, no. 11, pp.1462–1471, Sep. 2009.

[8] D. Isa and R. Rajkumar, “Pipeline Defect Prediction Using Support VectorMachines,” Applied Artificial Intelligence, vol. 23, no. 8, pp. 758–771,Sep. 2009.

[9] A. Moore, “SVM Tutorial Slides,” 2001. [Online]. Available: http://www.autonlab.org/tutorials/svm15.pdf