A new microcontrolled structural health monitoring system based on the electromechanical impedance principle

This article presents a new method to detect damage in structures based on the electromechanical impedance principle. The system follows the variations in the output voltage of piezoelectric transducers and does not compute the impedance itself. The proposed system is portable, autonomous, versatile, and could efficiently replace commercial instruments in different structural health monitoring applications. The identification of damage is performed by simply comparing the variations of root mean square voltage from response signals of piezoelectric transducers, such as lead zirconate titanate patches bonded to the structure, obtained for different frequencies of the excitation signal. The proposed system is not limited by the sampling rate of analog-to-digital converters, dispenses Fourier transform algorithms, and does not require a computer for processing, operating autonomously. A low-cost prototype based on microcontroller and digital synthesizer was built, and experiments were carried out on an aluminum structure and excellent results have been obtained. © The Author(s) 2012.

Real-time monitoring of ozone in air using substrate-integrated hollow waveguide mid-infrared sensors

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Structural damage identification and location using grammian matrices

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

A framework for monitoring and runtime recovery of web service-based applications

Service provisioning is a challenging research area for the design and implementation of autonomic service-oriented software systems. It includes automated QoS management for such systems and their applications. Monitoring, Diagnosis and Repair are three key features of QoS management. This work presents a self-healing Web service-based framework that manages QoS degradation at runtime. Our approach is based on proxies. Proxies act on meta-level communications and extend the HTTP envelope of the exchanged messages with QoS-related parameter values. QoS Data are filtered over time and analysed using statistical functions and the Hidden Markov Model. Detected QoS degradations are handled with proxies. We experienced our framework using an orchestrated electronic shop application (FoodShop).

Tigecycline: A Review of Properties, Applications, and Analytical Methods

Tigecycline is a new glycylcycline with an expanded broad-spectrum antibiotic, including inhibition of Gram-positive, Gram-negative, atypical, anaerobic, and antibiotic-resistant organisms. Trials have demonstrated that tigecycline is noninferior to the comparators for the treatment of complicated skin and skin structure infections as well as complicated intra-abdominal infections. Tigecycline is only available as an intravenous preparation and analytical methods to its quantitation in pharmaceutical products has not been published to date. This review examined tigecycline characteristics, the spectrum and mechanism of action, pharmacokinetics, applications, and, mainly, the instrumental conditions of published chromatographic methods used to measure tigecycline, its metabolites, and some analogs in clinical and biologic research.

A New Impedance Measurement System for PZT-Based Structural Health Monitoring

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Monitoring of cure kinetic prepreg and cure cycle modeling

Cure kinetic model is an integral part of composite process simulation, which is used to predict the degree of curing and the amount of the generated heat. The parameters involved in kinetic models are usually determined empirically from isothermal or dynamic differential scanning calorimetry (DSC) data. In this work, DSC and rheological techniques were used to investigate some of the kinetic parameters of cure reactions of carbon/F161 epoxy prepreg and to evaluate the cure cycle used to manufacture polymeric composites for aeronautical applications. As a result, it was observed that the F161 prepreg presents cure kinetic with total order 1.2-1.9. (c) 2006 Springer Science + Business Media, Inc.

Petroleum well drilling monitoring through cutting image analysis and artificial intelligence techniques

Petroleum well drilling monitoring has become an important tool for detecting and preventing problems during the well drilling process. In this paper, we propose to assist the drilling process by analyzing the cutting images at the vibrating shake shaker, in which different concentrations of cuttings can indicate possible problems, such as the collapse of the well borehole walls. In such a way, we present here an innovative computer vision system composed by a real time cutting volume estimator addressed by support vector regression. As far we know, we are the first to propose the petroleum well drilling monitoring by cutting image analysis. We also applied a collection of supervised classifiers for cutting volume classification. (C) 2010 Elsevier Ltd. All rights reserved.

Real-time optical monitoring of the dip coating process

A significant part of film production by the coating industry is based on wet bench processes, where better understanding of their temporal dynamics could facilitate control and optimization. In this work, in situ laser interferometry is applied to study properties of flowing liquids and quantitatively monitor the dip coating batch process. Two oil standards Newtonian, non-volatile, with constant refractive indices and distinct flow properties - were measured under several withdrawing speeds. The dynamics of film physical thickness then depends on time as t(-1/2), and flow characterization becomes possible with high precision (linear slope uncertainty of +/-0.04%). Resulting kinematic viscosities for OP60 and OP400 are 1,17 +/- 0,03. St and 9,9 +/- 0,2 St, respectively. These results agree with nominal values, as provided by the manufacturer. For more complex films (a multi-component sol-gel Zirconyl Chloride aqueous solution) with a varying refractive index, through a direct polarimetric measurement, allowing also determination of the temporal evolution of physical thickness (uncertainty of +/- 0,007 microns) is also determined during dip coating.

Real-time monitoring and kinetic parameter estimation of the affinity interaction of jArtinM and rArtinM with peroxidase glycoprotein by the electrogravimetric technique

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

Urban Traffic Control and Monitoring - An Approach for the Brazilian Intelligent Cities Project

This paper describes an urban traffic control system which aims at contributing to a more efficient traffic management system in the cities of Brazil. It uses fuzzy sets, case-based reasoning, and genetic algorithms to handle dynamic and unpredictable traffic scenarios, as well as uncertain, incomplete, and inconsistent information. The system is composed by one supervisor and several controller agents, which cooperate with each other to improve the system's results through Artificial Intelligence Techniques.

Fish cytogenetic research: Advances, applications and perspectives

Methods developed since 1976 for harvesting, preparing and banding fish chromosomes are now commonly used for taxonomic and phylogenetic studies, genetic control and chromosome manipulations in fish breeding and in monitoring aquatic pollutants by examining chromosomal aberrations. These studies have chiefly concerned common temperate freshwater species; the same procedures, when applied to marine and coldwater fish, often provide unsatisfactory results, especially in cell culture. A concerted effort should be made in marine fish, and to develop molecular cytogenetic methods to provide a more powerful tool to study chromosomal evolution. © 1991 BRILL.

Impedance-based structural health monitoring with artificial neural networks

This paper presents a non-model based technique to detect, locate, and characterize structural damage by combining the impedance-based structural health monitoring technique with an artificial neural network. The impedance-based structural health monitoring technique, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations (typically >30 kHz), this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors. In order to quantitatively assess the state of structures, multiple sets of artificial neural networks, which utilize measured electrical impedance signals for input patterns, were developed. By employing high frequency ranges and by incorporating neural network features, this technique is able to detect the damage in its early stage and to estimate the nature of damage without prior knowledge of the model of structures. The paper concludes with experimental examples, investigations on a massive quarter scale model of a steel bridge section and a space truss structure, in order to verify the performance of this proposed methodology.

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.

Monitoring in precision metal drilling process using multi-sensors and neural network

This paper presents a new method to estimate hole diameters and surface roughness in precision drilling processes, using coupons taken from a sandwich plate composed of a titanium alloy plate (Ti6Al4V) glued onto an aluminum alloy plate (AA 2024T3). The proposed method uses signals acquired during the cutting process by a multisensor system installed on the machine tool. These signals are mathematically treated and then used as input for an artificial neural network. After training, the neural network system is qualified to estimate the surface roughness and hole diameter based on the signals and cutting process parameters. To evaluate the system, the estimated data were compared with experimental measurements and the errors were calculated. The results proved the efficiency of the proposed method, which yielded very low or even negligible errors of the tolerances used in most industrial drilling processes. This pioneering method opens up a new field of research, showing a promising potential for development and application as an alternative monitoring method for drilling processes. © 2012 Springer-Verlag London Limited.