Rodolfo Villamizar

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.

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

Structural damage continuous monitoring by using a data driven approach based on principal component analysis and cross-correlation analysis

Continuous monitoring for damage detection in structural assessment comprises implementation of low cost equipment and efficient algorithms. This work describes the stages involved in the design of a methodology with high feasibility to be used in continuous damage assessment. Specifically, an algorithm based on a data-driven approach by using principal component analysis and pre-processing acquired signals by means of cross -correlation functions, is discussed. A carbon steel pipe section and a laboratory tower were used as test structures in order to demonstrate the feasibility of the methodology to detect abrupt changes in the structural response when damages occur. Two types of damage cases are studied: crack and leak for each structure, respectively. Experimental results show that the methodology is promising in the continuous monitoring of real structures.

Evaluation of Piezodiagnostics Approach for Leaks Detection in a Pipe Loop

Pipe leaks detection has a great economic, environmental and safety impact. Although several methods have been developed to solve the leak detection problem, some drawbacks such as continuous monitoring and robustness should be addressed yet. Thus, this paper presents the main results of using a leaks detection and classification methodology, which takes advantage of piezodiagnostics principle. It consists of: i) transmitting/sensing guided waves along the pipe surface by means of piezoelectric device ii) representing statistically the cross-correlated piezoelectric measurements by using Principal Component Analysis iii) identifying leaks by using error indexes computed from a statistical baseline model and iv) verifying the performance of the methodology by using a Self-Organizing Map as visualization tool and considering different leak scenario. In this sense, the methodology was experimentally evaluated in a carbon-steel pipe loop under different leaks scenarios, with several sizes and locations. In addition, the sensitivity of the methodology to temperature, humidity and pressure variations was experimentally validated. Therefore, the effectiveness of the methodology to detect and classify pipe leaks, under varying environmental and operational conditions, was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, and Self-Organizing Maps, become as promising solution in the field of structural health monitoring and specifically to achieve robust solution for pipe leak detection.

Temperature robust PCA based stress monitoring approach

In this paper, a guided wave temperature robust PCA-based stress monitoring methodology is proposed. It is based on the analysis of the longitudinal guided wave propagating along the path under stress. Slight changes in the wave are detected by means of PCA via statistical T2 and Q indices. Experimental and numerical simulations of the guided wave propagating in material under different temperatures have shown significant variations in the amplitude and the velocity of the wave. This condition can jeopardize the discrimination of the different stress scenarios detected by the PCA indices. Thus, it is proposed a methodology based on an extended knowledge base, composed by a PCA statistical model for different discrete temperatures to produce a robust classification of stress states under variable environmental conditions. Experimental results have shown a good agreement between the predicted scenarios and the real ones

Flow Estimation in a Steel Pipe Using Guided Waves

In this investigation, a flow rate estimation guided wave based scheme in pipes is proposed. The effect of the fluid over the propagation of longitudinal waves has been experimentally studied by using several laminar flows of water transported by a steel pipe. Results have shown a decrease of the guided wave pattern repeatability and the signal energy as the flow rate increase as a result of the energy leakage from the pipe to the fluid. A Matlab® script is used to excite the PZT actuator via picoscope 2208 of Picotech®, the captured signal is acquired also by the picoscope and the data is processed in Matlab. The test bench utilized is composed by a 1” sch 40 A-106 pipe, a needle valve and a centrifugal pump provides the flow energy. A couple of PZTs are used in a picth-catch configuration to produce and capture the longitudinal waves along the cross section of the pipe

Pipe leaks classification by using a data-driven approach based on features from cross-correlated piezo-vibration signals

This work presents a data driven approach for pipe leaks classification, validated on a steel carbon pipe section conditioned with leaks of different sizes and locations in order to emulate abnormal conditions. The tested structure was instrumented with piezoelectric devices attached at different locations over the surface, in order to induce guided waves and to record its behaviour along the structure. For each experiment, one piezo device is excited by means of a high frequency burst type signal and the other ones are used as sensors. A blind bridle is connected to one of the extremes and an air source is coupled to the other extreme to emulate operational conditions. Statistical indices of correlated piezoelectric signals are obtained by using principal component analysis to distinguish different leak scenarios. Next, a selforganizing map is used to classify them. The experimental results show an improvement of the classification-learning rate when correlated signals are used instead of uncorrelated ones

Study of cross-correlation signals in a data-driven approach for damage classification in aircraft wings

This paper discusses, experimental results of classifying several mass adding in a wing aircraft structure, using cross-correlated piezoelectric signals, represented by principal components. Piezoelectric signals are applied and recorded at specific points of the structure under analysis. Then, statistical features are obtained by means of principal component analysis to the correlation between excitation and response signals. Unsupervised learning is implemented to the reduced feature space, in order to identify clusters of damaged cases. The main result of this paper is the advantage resulting from using cross-correlated signals, evaluated through the performance of clustering indexes. Experimental data are collected from two test structures: i.) A turbine blade of a commercial aircraft and ii.) The skin panel of the torsion box of a wing. Damages are induced adding masses at different locations of the wing section surface. The results obtained show the effectiveness of the methodology to distinguish between different damage cases.