Viviana Meruane

Damage Detection with Parallel Genetic Algorithms and Operational Modes

The detection of damage with model-based methods is a constrained nonlinear optimization problem. Conventional optimization approaches usually lead to local minima. Furthermore, they are highly sensitive to experimental noise or numerical errors. Genetic algorithms (GAs) provide an attractive alternative since they can potentially explore the entire solution space and reach the global optimum. However, GAs are inherently slow when they work with complicated or time consuming objective functions. To overcome this problem parallel GAs are proposed, and they are particularly easy to implement and provide a superior numerical performance. In this study, a real-coded parallel GA is implemented to detect structural damage. The objective function is based on operational modal data; it considers the initial errors in the numerical model. False damage detection is avoided by using damage penalization. The algorithm is verified with two experimental cases. First, a test structure of an airplane subjected to three increasing levels of damage. Second, a multiple cracked reinforced concrete beam that is subjected to a nonsymmetrical increasing static load to introduce cracks. In both cases, the detected damage has a good correspondence with the experimental damage.

On the effect of downtime costs and budget constraint on preventive and replacement policies

This work proposes a general approach to study and improve the effectiveness of the system with respect to its expected life-cycle cost rate. The model we propose considers a production system which is protected against demand fluctuations and failure occurrences with elements like stock piles, line and equipment redundancy, and the use of alternative production methods. These design policies allow to keep or minimize the effect on the nominal throughput, while corrective measures are taken. The system is also subject to an aging process which depends on the frequency and quality of preventive actions. Making decisions is difficult because of discontinuities in intervention and downtime costs and the limited budget. We present a non-linear mixed integer formulation that minimizes the expected overall cost rate with respect to repair, overhaul and replacement times and the overhaul improvement factor proposed in the literature. The model is deterministic and considers minimal repairs and imperfect overhauls. We illustrate its application with a case based on a known benchmark example.

Structural damage assessment under varying temperature conditions

Modal parameters such as natural frequencies and mode shapes are sensitive indicators of structural damage. However, they are not only sensitive to damage, but also to the environmental conditions such as, humidity, wind and most important, temperature. For civil engineering structures, modal changes produced by environmental conditions can be equivalent or greater than the ones produced by damage. This article proposes a damage detection method which is able to deal with temperature variations. The objective function correlates mode shapes and natural frequencies, and a parallel genetic algorithm handles the inverse problem. The numerical model of the structure assumes that the elasticity modulus of the materials is temperature-dependent. The algorithm updates the temperature and damage parameters together. Therefore, it is possible to distinguish between temperature effects and real damage events. Simulated data of a three-span bridge and experimental one of the I-40 Bridge validate the proposed methodology. Results show that the proposed algorithm is able to assess the experimental damage despite of temperature variations.

A Broadband Vibration-Based Energy Harvester Using an Array of Piezoelectric Beams Connected by Springs

Piezoelectric cantilevered beams have been widely used as vibration-based energy harvesters. Nevertheless, these devices have a narrow frequency band and if the excitation is slightly different there is a significant drop in the level of power generated. To handle this problem, the present investigation proposes the use of an array of piezoelectric cantilevered beams connected by springs as a broadband vibration-based energy harvester. The equations for the voltage and power output of the system are derived based on the analytical solution of the piezoelectric cantilevered energy harvester with Euler-Bernoulli beam assumptions. To study the advantages and disadvantages of the proposed system, the results are compared with those of an array of disconnected beams (with no springs). The analytical model is validated with experimental measurements of three bimorph beams with and without springs. The results show that connecting the array of beams with springs allows increasing the frequency band of operation and increasing the amount of power generated.

Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings

Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Time-frequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

Real-Time Structural Damage Assessment Using Artificial Neural Networks and Antiresonant Frequencies

The main problem in damage assessment is the determination of how to ascertain the presence, location, and severity of structural damage given the structures dynamic characteristics. The most successful applications of vibration-based damage assessment are model updating methods based on global optimization algorithms. However, these algorithms run quite slowly, and the damage assessment process is achieved via a costly and time-consuming inverse process, which presents an obstacle for real-time health monitoring applications. Artificial neural networks (ANN) have recently been introduced as an alternative to model updating methods. Once a neural network has been properly trained, it can potentially detect, locate, and quantify structural damage in a short period of time and can therefore be applied for real-time damage assessment. The primary contribution of this research is the development of a real-time damage assessment algorithm using ANN and antiresonant frequencies. Antiresonant frequencies can be identified more easily and more accurately than mode shapes, and they provide the same information. This research addresses the setup of the neural network parameters and provides guidelines for the selection of these parameters in similar damage assessment problems. Two experimental cases validate this approach: an 8-DOF mass-spring system and a beam with multiple damage scenarios.

Impact Location and Quantification on an Aluminum Sandwich Panel Using Principal Component Analysis and Linear Approximation with Maximum Entropy

To avoid structural failures it is of critical importance to detect, locate and quantify impact damage as soon as it occurs. This can be achieved by impact identification methodologies, which continuously monitor the structure, detecting, locating, and quantifying impacts as they occur. This article presents an improved impact identification algorithm that uses principal component analysis (PCA) to extract features from the monitored signals and an algorithm based on linear approximation with maximum entropy to estimate the impacts. The proposed methodology is validated with two experimental applications, which include an aluminum plate and an aluminum sandwich panel. The results are compared with those of other impact identification algorithms available in literature, demonstrating that the proposed method outperforms these algorithms.

Ensemble classification method for structural damage assessment under varying temperature

Vibration-based damage assessment approaches use modal parameters, such as frequency response functions, mode shapes, and natural frequencies, as indicators of structural damage. Nevertheless, these parameters are sensitive not only to damage but also to temperature variations. Most civil engineering structures are exposed to varying environmental conditions, thus hindering vibration-based damage assessment. Therefore, in this article, a new damage assessment algorithm based on pattern recognition is proposed to scrutinize the healthy state of a structure in the presence of uncertainties such as noise and temperature. The algorithm adopts a combination of couple sparse coding and deep neural network as an ensemble system to assess damage. The proposed method is validated using a numerical model of a truss bridge and experimental data of the I-40 bridge. The results demonstrate its efficiency in the localization and quantification of damages under varying temperature conditions.

Prediction of leak flow rate in plastic water distribution pipes using vibro-acoustic measurements:

Leakage from water distribution systems is a worldwide issue with consequences including loss of revenue, health and environmental concerns. Leaks have typically been found through leak noise correlation by placing sensors either side of the leak and recording and analysing its vibro-acoustic emission. While this method is widely used to identify the location of the leak, the sensors also record data that could be related to the leak’s flow rate, yet no reliable method exists to predict leak flow rate in water distribution pipes using vibro-acoustic emission. The aim of this research is to predict leak flow rate in medium-density polyethylene pipe using vibro-acoustic emission signals. A novel experimental methodology is presented whereby circular holes of four sizes are tested at several leak flow rates. Following the derivation of a number of features, least squares support vector machines are used in order to predict leak flow rate. The results show a strong correlation highlighting the potential of this technique as a rapid and practical tool for water companies to assess and prioritise leak repair.

The effect of molecular weight and hydrolysis degree of poly(vinyl alcohol)(PVA) on the thermal and mechanical properties of poly(lactic acid)/PVA blends

Comision Nacional de Investigacion Cientifica y Tecnologica CONICYT (Beca de Doctorado Nacional - Proyecto) PAI 781411004 CONICYT-REGIONAL R08C1002 Programa de Financiamiento Basal para Centros Cientificos y Tecnologicos de Excelencia PFB-27