846 resultados para health monitoring
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Corrosion represents one of the largest through life cost component of ships. Ship owners and operators recognize that combating corrosion significantly impacts the vessels’ reliability, availability and through life costs. Primary objective of this paper is to review various inspections, monitoring systems and life cycle management with respect to corrosion control of ships and to develop the concept of “Corrosion Health” (CH) which would quantify the extent of corrosion at any point of ships’ operational life. A system approach in which the ship structure is considered as a corrosion system and divided into several corrosion zones, with distinct characteristics, is presented. Various corrosion assessment criteria for assessment of corrosion condition are listed. A CH rating system for representation of complex corrosion condition with a numeric number along with recommendations for repair/maintenance action is also discussed
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Health monitoring technologies such as Body Area Network (BAN) systems has gathered a lot of attention during the past few years. Largely encouraged by the rapid increase in the cost of healthcare services and driven by the latest technological advances in Micro-Electro-Mechanical Systems (MEMS) and wireless communications. BAN technology comprises of a network of body worn or implanted sensors that continuously capture and measure the vital parameters such as heart rate, blood pressure, glucose levels and movement. The collected data must be transferred to a local base station in order to be further processed. Thus, wireless connectivity plays a vital role in such systems. However, wireless connectivity comes at a cost of increased power usage, mainly due to the high energy consumption during data transmission. Unfortunately, battery-operated devices are unable to operate for ultra-long duration of time and are expected to be recharged or replaced once they run out of energy. This is not a simple task especially in the case of implanted devices such as pacemakers. Therefore, prolonging the network lifetime in BAN systems is one of the greatest challenges. In order to achieve this goal, BAN systems take advantage of low-power in-body and on-body/off-body wireless communication technologies. This paper compares some of the existing and emerging low-power communication protocols that can potentially be employed to support the rapid development and deployment of BAN systems.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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This paper presents a non-model based technique to detect and locate structural damage with the use of artificial neural networks. This method utilizes high frequency structural excitation (typically greater than 30 kHz) through a surface-bonded piezoelectric sensor/actuator to detect changes in structural point impedance due to the presence of damage. Two sets of artificial neural networks were developed in order to detect, locate and characterize structural damage by examining changes in the measured impedance curves. A simulation beam model was developed to verify the proposed method. An experiment was successfully performed in detecting damage on a 4-bay structure with bolted-joints, where the bolts were progressively released.
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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.
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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.
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In this paper we present a versatile and easy-to-assemble measurement system for structural health monitoring (SHM) based on the electromechanical impedance (EMI) technique. The hardware of the proposed system consists only of a common data acquisition (DAQ) device with external resistors and allows real-time data acquisition from multiple sensors. Besides the low-cost compared to conventional impedance analyzers, the hardware and the software are simple and easier to implement than other measurement systems that have been recently proposed.
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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.