Luis Eduardo Mujica

Impact Damage Detection in Aircraft Composites Using Knowledge-based Reasoning

A hybrid reasoning methodology is applied to a complex aerospace structure, and its effectiveness is assessed in identifying and locating the position of impacts. Part of a commercial aircraft wing flap is impacted and time-varying strain response data from the structure are sensed using passive piezoceramic sensors. This structure can be regarded as a small scale version of part of a wing span with the corresponding features being a leading edge and trailing edge. The trailing edge is composed of aluminium skins with an aluminium honeycomb core, the leading edge of composite skins with a light weight honeycomb core, and the central section of thin composite material. Nine sensors, to detect time-varying strain response data, are distributed over the surface of the flap; two on the leading edge, two on the trailing edge, and five in the central section. The methodology combines the use of: (i) Case-Based Reasoning; in a `learning mode, an initial casebase is created with the principal features of the impact responses. When the system is working in an `operating mode, the data acquired by sensors are used to perform a diagnosis by analogy with the cases stored in the casebase: reusing and adapting old situations. (ii) The Wavelet Transform is used to extract principal features of a signal providing information about the impact locations. (iii) Self-Organizing Maps are trained as a classification tool in order to organize the old cases in memory with the purpose of speeding up the reasoning process. Finally, when old similar cases are retrieved, the impact location is obtained directly from heuristic considerations.

Two Approaches to Structural Damage Identification: Model Updating versus Soft Computing

This article presents two approaches for structural damage identification, each based on a different philosophy. The virtual distortion method (VDM) is a model-updating method of damage assessment, utilizing gradient-based optimization techniques to solve the resulting inverse dynamic problem in the time domain. Case-based reasoning (CBR) is a softcomputing method utilizing wavelet transformation for signal processing and neural networks for training a base of damage cases to use for retrieving a similar relevant case. Advantages and drawbacks of each approach are discussed. Successful calibration of a numerical model from experiments has been shown as a sin equa non for the VDM approach. A numerical example of a beam is presented including a demonstration of the complexity of the inverse problem. Qualitative and quantitative comparisons between the two approaches are made.

Comparison of two robust PCA methods for damage detection in presence of outliers

Statistical methods such as Principal Component Analysis (PCA) are suffering from contaminated data. For instance, variance and covariance as vital parts of PCA method are sensitive to anomalous observation called outliers. Outliers, who are usually, appear due to experimental errors, are observations that lie at a considerable distance from the bulk of the observations. An effective way to deal with this problem is to apply a robust, i.e. not sensitive to outliers, variant of PCA. In this work, two robust PCA methods are used instead of classical PCA in order to construct a model using data in presence of outliers to detect and distinguish damages in structures. The comparisons of the results shows that, the use of the mentioned indexes based on the robust models, distinguish the damages much better than using classical one, and even in many cases allows the detection where classic PCA is not able to discern between damaged and non-damaged structure. In addition, two robust methods are compared with each other and their features are discussed. This work involves experiments with an aircraft turbine blade using piezoelectric transducers as sensors and actuators and simulated damages.

PCA Based Stress Monitoring of Cylindrical Specimens Using PZTs and Guided Waves

Since mechanical stress in structures affects issues such as strength, expected operational life and dimensional stability, a continuous stress monitoring scheme is necessary for a complete integrity assessment. Consequently, this paper proposes a stress monitoring scheme for cylindrical specimens, which are widely used in structures such as pipelines, wind turbines or bridges. The approach consists of tracking guided wave variations due to load changes, by comparing wave statistical patterns via Principal Component Analysis (PCA). Each load scenario is projected to the PCA space by means of a baseline model and represented using the Q-statistical indices. Experimental validation of the proposed methodology is conducted on two specimens: (i) a 12.7 mm (1/2″) diameter, 0.4 m length, AISI 1020 steel rod, and (ii) a 25.4 mm (1″) diameter, 6m length, schedule 40, A-106, hollow cylinder. Specimen 1 was subjected to axial loads, meanwhile specimen 2 to flexion. In both cases, simultaneous longitudinal and flexural guided waves were generated via piezoelectric devices (PZTs) in a pitch-catch configuration. Experimental results show the feasibility of the approach and its potential use as in-situ continuous stress monitoring application.

In-line Inspection of Pipelines by Using a Smart Pig (ITION) and Multivariate Statistical Analysis

The ferrous pipe structures of oil and gas production and, the transmission pipelines are, in majority, buried. Nowadays, phenomena like corrosion, mechanical stress, soil erosion, worker mistakes and damages caused by third parts have generated several problems over pipelines. Thus, major investment on integrity programs with In-Line Inspection Tools has been improved in order to examine the pipelines and avoid environmental, financial and social disasters. Recently in Colombia, the Research Institute of Corrosion - CIC (Corporacion para la Investigacion de la Corrosion) runs their own smart pig ILI tool in pipelines. The inspection technology is based on inertial and operational trends, ITION (Inertial Technology Inspection and Operational Trends). Up to date, the technology has been tested several times inside of pipelines providing valuable information along of thousand kilometres. These records contain a huge amount of data that sometimes is difficult or impossible to understand by themselves. A univariate statistical analysis can be used to determine the thresholds for each observation variable. However, it does not analyse the correlated information between them. In this way, the main contribution of this work is the development of a methodology based on Principal Component Analysis (PCA) to monitor the structure by using the whole available variables gathered by ITION. doi: 10.12783/SHM2015/291

Extended PCA visualisation of system damage features under environmental and operational variations

This paper explores the use of Principal Component Analysis (PCA), an extended form of PCA and, the T2- statistic and Q-statistic; distances that detect and distinguish damages in structures under varying operational and environmental conditions. The work involves an experiment in which two piezoelectric transducers are bonded on an aluminium plate. The plate is subjected to several damages and exposed to different levels of temperature. A series of tests have been performed for each condition. The approach is able to determine whether the structure has damage or not, and besides, gives qualitative information about its size, isolating effects of the temperature.

Non-destructive testing for assessing structures by using soft-computing

A hybrid system which combines Self Organizing Maps and Case Based Reasoning is presented and apply to Structural Assessment. Self Organizing Maps are trained as a classification tool in order to organize the old cases in memory with the purpose of speeding up the Case Based Reasoning process. Three real structures have been used: An aluminium beam, a pipe section and a long pipe.

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

This work discusses the advantage of using cross-correlation analysis in a data-driven approach based on principal component analysis (PCA) and piezodiagnostics to obtain successful diagnosis of events in structural health monitoring (SHM). In this sense, the identification of noisy data and outliers, as well as the management of data cleansing stages can be facilitated through the implementation of a preprocessing stage based on cross-correlation functions. Additionally, this work evidences an improvement in damage detection when the cross-correlation is included as part of the whole damage assessment approach. The proposed methodology is validated by processing data measurements from piezoelectric devices (PZT), which are used in a piezodiagnostics approach based on PCA and baseline modeling. Thus, the influence of cross-correlation analysis used in the preprocessing stage is evaluated for damage detection by means of statistical plots and self-organizing maps. Three laboratory specimens were used as test structures in order to demonstrate the validity of the methodology: (i) a carbon steel pipe section with leak and mass damage types, (ii) an aircraft wing specimen, and (iii) a blade of a commercial aircraft turbine, where damages are specified as mass-added. As the main concluding remark, the suitability of cross-correlation features combined with a PCA-based piezodiagnostic approach in order to achieve a more robust damage assessment algorithm is verified for SHM tasks.

Support Stiffness Monitoring of Cylindrical Structures Using Magnetostrictive Transducers and the Torsional Mode T(0,1)

In this paper, a support stiffness monitoring scheme based on torsional guided waves for detecting loss of rigidity in a support of cylindrical structures is presented. Poor support performance in cylindrical specimens such as a pipeline setup located in a sloping terrain may produce a risky operation condition in terms of the installation integrity and the possibility of human casualties. The effects of changing the contact forces between support and the waveguide have been investigated by considering variations in the load between them. Fundamental torsional T(0,1) mode is produced and launched by a magnetostrictive collar in a pitch-catch configuration to study the support effect in the wavepacket propagation. Several scenarios are studied by emulating an abnormal condition in the support of a dedicated test bench. Numerical results revealed T(0,1) ultrasonic energy leakage in the form of SH0 bulk waves when a mechanical coupling between the cylindrical waveguide and support is yielded. Experimental results showed that the rate of ultrasonic energy leakage depends on the magnitude of the reaction forces between pipe and support; so different levels of attenuation of T(0,1) mode will be produced with different mechanical contact conditions. Thus, it is possible to relate a measured attenuation to variations in the supports condition. Results of each scenarios are presented and discussed demonstrating the feasibility and potential of tracking of the amplitude of the T(0,1) as an indicator of abnormal conditions in simple supports.

Torsional Waves for Load Monitoring of Cylindrical Waveguides

This paper is aimed to produce a methodology to monitoring changes in the load condition in the support based on the understanding of how the mode T(0,1) interacts with simple supports under varying load conditions. An analytical expression of the phase velocity for the fundamental torsional mode T(0,1) propagating in a medium under mechanical stress is derived based on the acoustoelasticity effect. A test bench is implemented to emulate a failure or a change in the loading support conditions. A scheme pitch-catch is adopted taking advantage of an affordable way to produce torsional waves in situ based on the magnetostriction effect