MANUEL PINTO

PhD Graduate

PhD program:: XXXIII


advisor: Antonio Carcaterra

Thesis title: IDENTIFICATION: CATCHING THE INFORMATION VIA THE DEPLOYMENT OF ACTIVE SENSORS

The research activity has its focus on the study of swarm of active sensors and data fusion for identification problem. A swarm of sensors generates a large amount of information; the core of this research is to manipulate and control that information so to identify and reconstruct the actual state of the observed system. When we deal with swarm of sensors the main problem is how to handle the information that arise from each agent that could be corrupted or non-directly measurable. We have indeed data arising from objects that are near to each other, subjected e.g. to the environmental conditions or external noise. This implies the obtained signals, related to the behavior of the observed system, can be delayed, or in some cases valuable information could get lost. Based on these assumptions, relevant questions arise: how is it possible to recover the state of the observed system based only on partial information? What is the most efficient methodology to adopt to recover the configuration of the swarm and so the state of the investigated system? The main goal of the research activity is to define a general methodology, based on Bayesian theory, able to infer those information from the analysis of the signal acquired by the swarm of sensors. The intention is to determine the most probable configuration of the system, based on some time-dependent measurements acquired by each agent of the sensor swarm. Typically, the system of sensors is placed within an environment and the position of each sensor is assumed fixed. One peculiar category of these kind of problems takes place in the context of structural monitoring and deals with the identification of structural anomalies within complex system. In this type of applications, indeed, the possibility of monitoring the temporal behavior of mechanical structures has a considerable importance since it can unveil, based on the observations performed at the present time, conditions that can rapidly turn into critical. In the first part of this research project this problem has been investigated. In particular, the aim is to study innovative structural elements able to reinforce large civil structures. It is proposed the monitoring of the train bridge on the Bormida River (northern Italy). The project was financially supported by BASF Italy. Accordingly, the analysis of the strain along a beam that is equipped with Glass Fibers Reinforced Polymers (GFRP) with an embedded array of optical Fiber Bragg Grating (FBG) sensors has been studied. Because of the production process of such elements, it is impossible to locate the FBG along the reinforcement bar and consequently the actual position of the measurement points becomes an unknown of the problem. This alters the idea of the standard identification problem; indeed, the unknown location of the measurement stations became an element of the identification process itself. For this reason, the first part of the thesis focuses on the development of a technique for the identification of the unknown placements of the array of sensors. This technique is based on features extracted by the modal approximation of the elastic shape of the monitored structure. Those features are the inputs of an Artificial Neural Network, which permits to predict the sensor location using strain measurements only. The second part of this work considers a more general scenario in which the sensors have a controlled motion within the field. If the sensors have their own dynamics, it is reasonable to optimally control their motion in order to identify emerging characteristic of the observed elastic field (whether elastic or gaseous). The case of study deals with an acoustic field propagating along a region over the space. This case does not differ much from the monitoring of large structures: in both cases, the presence of a source represents a discontinuity in the field to be identified. The last part of the thesis proposes a merge between the first two topics of study. The idea of an array of moving sensors in the context of structural health monitoring is investigated to propose a new approach for the detection of irregularities within large structures. The goal of the method is to optimally control the motion of the swarm, driving the sensors in more optimal measurement regions, in order to perceive the field as clean as possible and to enhance the identification of any possible irregularity along the structure. The statement of the proposed problem does not differ so much from the one of the identification problems so far discussed. Indeed, in this kind of modeling, the damage can be considered as a source of discontinuity within the field (which in this case is the elastic field), that needs to be identified. The problem is tackled in a two-steps strategy, namely: i) driving module, based on a LQR control action in order to drive the available sensors along different constrained regions of the system and ii) damage identification module, which is based on the processing of the acquired data with the Ensemble Empirical Mode Decomposition combined with the Hilbert-Huang transform, to identify the irregularity within the analyzed structure. All these problems are analysed through numerical simulations, basis for the experimental campaign performed during the research period.

Research products

  • 11573/1686586 - 2023 - Mobile self-driving sensors for identification of vibration and acoustic fields (04b Atto di convegno in volume)
    PINTO, MANUEL; PEPE, GIANLUCA; ROVERI, NICOLA; CULLA, ANTONIO; CARCATERRA, ANTONIO
  • 11573/1190714 - 2019 - Extraction of the beam elastic shape from uncertain FBG strain measurement points (04b Atto di convegno in volume)
    PINTO, MANUEL; ROVERI, NICOLA; PEPE, GIANLUCA; CARCATERRA, ANTONIO
  • 11573/1686556 - 2022 - A New Approach for Structural Health Monitoring: Damage Detection on Large Structures through a Swarm of Moving Sensors (04b Atto di convegno in volume)
    PINTO, MANUEL; ROVERI, NICOLA; PEPE, GIANLUCA; CARCATERRA, ANTONIO
  • 11573/1308921 - 2018 - Embedded optical sensors for vibration monitoring of large structures (04b Atto di convegno in volume)
    PINTO, MANUEL; ROVERI, NICOLA; PEPE, GIANLUCA; CARCATERRA, ANTONIO
  • 11573/1310709 - 2019 - Swarm of robot attacking an acoustic source: detection and trapping (04b Atto di convegno in volume)
    PINTO, MANUEL; PEPE, GIANLUCA; ROVERI, NICOLA; CARCATERRA, ANTONIO

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