Structural Health Monitoring based on AR models and PZT sensors

This paper presents a new approach for damage detection in Structural Health Monitoring (SHM) systems, which is based on the Electromechanical Impedance (EMI) principle and Autoregressive (AR) models. Typical applications of EMI in SHM are based on computing the Frequency Response Function (FRF). In this work the procedure is based on the EMI principle but the results are determined through the coefficients of AR models, which are computed from the time response of PZT transducers bonded to the monitored structure, and acting as actuator and sensors at the same time. The procedure is based on exciting the PZT transducers using a wide band chirp signal and getting its time response. The AR models are obtained in both healthy and damaged conditions and used to compute statistics indexes. Practical tests were carried out in an aluminum plate and the results have demonstrated the effectiveness of the proposed method. © 2012 IEEE.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Structural Health Monitoring in Smart Structures Through Time Series Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

A genetic neuro-model reference adaptive controller for petroleum wells drilling operations

Motivated by rising drilling operation costs, the oil industry has shown a trend towards real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated to parameters modeling. One of the drill-bit performance evaluators, the Rate of Penetration (ROP), has been used in the literature as a drilling control parameter. However, the relationships between the operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on the Auto-Regressive with Extra Input Signals model, or ARX model, to accomplish the system identification and on a Genetic Algorithm (GA) to provide a robust control for the ROP. Results of simulations run over a real offshore oil field data, consisted of seven wells drilled with equal diameter bits, are provided. © 2006 IEEE.

Structural damage detection by fuzzy clustering

The development of strategies for structural health monitoring (SHM) has become increasingly important because of the necessity of preventing undesirable damage. This paper describes an approach to this problem using vibration data. It involves a three-stage process: reduction of the time-series data using principle component analysis (PCA), the development of a data-based model using an auto-regressive moving average (ARMA) model using data from an undamaged structure, and the classification of whether or not the structure is damaged using a fuzzy clustering approach. The approach is applied to data from a benchmark structure from Los Alamos National Laboratory, USA. Two fuzzy clustering algorithms are compared: fuzzy c-means (FCM) and Gustafson-Kessel (GK) algorithms. It is shown that while both fuzzy clustering algorithms are effective, the GK algorithm marginally outperforms the FCM algorithm. (C) 2008 Elsevier Ltd. All rights reserved.

Applying a genetic neuro-model reference adaptive controller in drilling optimization

Motivated by rising drilling operation costs, the oil industry has shown a trend toward real-time measurements and control. In this scenario, drilling control becomes a challenging problem for the industry, especially due to the difficulty associated with parameters modeling. One of the drillbit performance evaluators, the Rate Of Penetration (ROP), has been used as a drilling control parameter. However, relationships between operational variables affecting the ROP are complex and not easily modeled. This work presents a neuro-genetic adaptive controller to treat this problem. It is based on an auto-regressive with extra input signals, or ARX model and on a Genetic Algorithm (GA) to control the ROP. © [2006] IEEE.

Adaptive filter feature identification for structural health monitoring in aeronautical panel

This paper presents an approach for structural health monitoring (SHM) by using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage to use a RLS filter is the clear possibility to perform an online SHM procedure since that the identification is also valid for non-stationary linear systems. An online damage-sensitive index feature is computed based on autoregressive (AR) portion of coefficients normalized by the square root of the sum of the square of them. The proposed method is then utilized in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. A hypothesis test employing the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize the damages simulated in the structure. The results have shown the applicability and drawbacks the method and the paper concludes with suggestions to improve it. ©2010 Society for Experimental Mechanics Inc.

Modelos explicativos em saúde coletiva: abordagem biopsicossocial e auto-organização

A complexidade do processo saúde-doença tem ensejado a proposição de uma diversidade de modelos explicativos. Fazemos uma breve revisão dessas propostas, confrontando três perspectivas: o modelo oriundo da Medicina do século XIX, a lógica da História Natural da Doença e o debate epidemiológico no contexto da Medicina Social latino-americana. Tomando-se como referência teórica a ideia de causalidade circular presente na teoria da auto-organização, propomos que os fatores causais privilegiados em cada um dos modelos explicativos acima não seriam conflitantes. Uma noção-chave para se pensar o processo de autoorganização biopsicossocial é o efeito baldwiniano, que descreve uma relação dialética ou coevolutiva entre processos naturais e socioculturais.

Generalization of Wye and Delta auto-connections: Analysis of voltages and currents

Multipulse rectifier topologies based on autoconnections, or differential connections, are more and more applied as interface stages between the mains and power converters. These topologies mitigate many low-order current harmonics in the utility, reducing the THD (total harmonic distortion) and increasing the power factor. This paper presents a mathematical model based on phasor diagrams, that results in a single expression able to unify all differential topologies connections (Delta and Wye), for both step-up or step-down autotransformers, for 12 and 18-pulse AC-DC converters. The proposed family of converters can be designed for any relationship between the input voltage and the load voltage. An immediate application would be the retrofit, i.e. to replace a conventional rectifier with poor quality of the processed energy by the 12 or 18 pulses rectifier with Wye or Delta-differential connection. The design procedure, simple and fast, is developed and tested for a prototype rating 6 kW and 250 V on the DC load © 2010 IEEE.

Analysis of inventory management in small business auto parts

The objective of this study was to analyze the production process and supply control in order to identify possible gaps and develop a method for managing supplies. The relevance of this research is on the benefits that can obtain by identifying the problems of supply control. The research method used was the case study, which was grounded on tripod semi-structured interviews, on-site observation, and document analysis. This methodology was very suitable because it can be analyzed and cross checked. The possibility of implementation of the proposal obtained from the theoretical framework, that together with the complementary actions suggested here, offers the opportunity to make the process more productive and profitable. This work allowed one to observe the weaknesses in managing the supply chain and at what points to work should be improved. It allowed to use some scientific models in the company object of study in order to improve supply management. © 2011 IEEE.

A new design methodology for multipulse rectifiers with Delta auto-connected transformers and a retrofit application in Adjustable Speed Drives (ASDs)

Multipulse rectifiers can replace a conventional six pulse three-phase rectifier (diode bridge) providing a DC voltage with low ripple, low Total Harmonic Distortion of current (THDi) and a high Power Factor (PF). In this context is presented a multipulse rectifier with generalized Delta-differential autotransformer topology, which can provide any level of DC output voltage for any level of three-phase AC input voltage. This paper presents all the possible configurations for Delta topology in order to choose, through graphics, one configuration that presents reduced weight and volume. The average voltage on the DC bus must be compatible with the DC voltage in the six pulse rectifier used in commercial ASDs. Therefore, it is possible to apply the retrofit technique to replace the conventional bridge rectifier by the proposed multipulse rectifier. Based on mathematic models and simulation results, an 18-pulse rectifier with Delta topology, 220 V of line voltage, 315 V of DC output, and rating 2.5 kW of power was designed, implemented and applied for three different commercial ASDs. Experimental results as voltage and current waveforms and results about PF and THDi will be presented. © 2012 IEEE.

Atividade física e doença de Parkinson: mudança de comportamento, auto-eficácia e barreiras percebidas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Processos auto-organizados em agregados de larvas de moscas-varejeiras em dispersão pós-alimentar: experimentação, modelagem e validação

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Análise de teses internalistas subjacentes à modelagem computacional da mente em uma perspectiva situada, incoporada e auto organizada

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)