tesi R. Valiante.pdf - EleA@UniSA

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10 1. INTRODUCTION All civil, mechanical and aerospace structures are subject to damage as a result of fatigue, overloading conditions, material degradation through environmental effects and unanticipated discrete events such as impacts or seismic events. Damage compromises the ability of the structure to perform its primary functions. Therefore, to ensure performance standards, extend the operational lifespan and maintain life-safety, many structural systems undergo routine inspections and maintenance. Common non-destructive evaluation (NDE) methods for evaluating the integrity of a structural component include the use of Eddy currents, acoustic emission, ultrasonic inspection, radiography, thermography or just basic visual inspections [1]. Depending upon the structural system, the cost associated with systematic time-based NDE inspection and maintenance can be substantial relative to the total life-cycle costs of the system. For commercial and military aircraft, it is estimated that 27% of the average life cycle costs are related to inspection and repair [2]. In addition, there is a corresponding opportunity cost associated with the loss in operational availability during maintenance. Within the aircraft industry in particular, maintenance procedures are dependent upon the design methodology adopted for the vehicle components. The two dominant design methodologies are safe-life and damage tolerant [1]. For safe-life design, the operational lifespan of structural components is estimated through a statistical analysis. Inspection is not necessary for this methodology, since the components are simply replaced prior to its specified design life. As a result, safe-life design is the least economical. Currently, many aircrafts are designed according to damage tolerant methodology. In this approach, models are used to predict the emergence of damage in various components under specified loading conditions. Based upon these models, NDE (Non Destructive Evaluation) inspection and preventive maintenance is performed at frequent time intervals to identify incipient damage and to prevent critical damage growth. An effort is currently being made to implement a newer condition based design methodology. In this case, maintenance is only performed when the system has undergone damage beyond a tolerable level.
INTRODUCTION 11 Ideally, routinely scheduled inspection would be replaced by the integration of a built-in structural health monitoring (SHM) system that can perform continuous on-line diagnostics as well as prognostics. The objective for the diagnostics is to perform damage identification while the prognostics allows for an estimation of the remaining useful life of the system. Condition based maintenance, based upon a SHM philosophy, is the most economical option. Ideally, an SHM system will increase the operational availability, extend the lifespan of an aircraft, enhance life-safety and dramatically reduce life-cycle costs. It is estimated that a savings of 40% on inspection time and 20% on inspection/maintenance costs could result from the implementation of a properly designed SHM system [1]. The function of an SHM system is to collect periodically sampled dynamic response measurements of the structure using an integrated network of strategically placed transducers [3]. From these measurements, extracted damage sensitive features are then used to solve a forward or inverse problem with system models or applied in statistical pattern recognition algorithms in order to evaluate the structural condition of the system. One of the primary objectives of an SHM system is damage identification. Damage identification can be organized into a hierarchical pattern as follows [4]: Level 1 (Damage Detection): Recognition that damage exists within the structure Level 2 (Damage Localization): identification of the geometric position of damage Level 3 (Damage Classification): Classification of damage type when multiple damage scenarios exist Level 4 (Damage Assessment): Quantification of damage extent Level 5 (Damage Prediction): Estimation of residual life of the structure.
Page 1: Unione Europea Università degli St
Page 5 and 6: RINGRAZIAMENTI E’ per me doveroso
Page 7: Vorrei, infine, disporre del vocabo
Page 11 and 12: CONTENTS Abstract of dissertation .
Page 13 and 14: LIST OF FIGURES AND TABLES Fig. 1.1
Page 15 and 16: Fig. 3.32 - : RMS of the field of r
Page 17 and 18: ABSTRACT OF DISSERTATION 7 ABSTRACT
Page 19: ABSTRACT OF DISSERTATION 9 are: The
Page 23 and 24: INTRODUCTION 13 Feature extraction
Page 25 and 26: INTRODUCTION 15 1.3. GLOBAL VERSUS
Page 27 and 28: INTRODUCTION 17 damage. These inves
Page 29 and 30: INTRODUCTION 19 between shifts at v
Page 31 and 32: INTRODUCTION 21 Space Shuttle Orbit
Page 33 and 34: INTRODUCTION 23 1.6. GUIDED WAVES-B
Page 35 and 36: INTRODUCTION 25 Fig. 1.3 - Differen
Page 37 and 38: INTRODUCTION 27 Considering the arg
Page 39 and 40: INTRODUCTION 29 Fig. 1.6 - Differen
Page 41 and 42: INTRODUCTION 31 2 ω c 2 k = 2 ω
Page 43 and 44: INTRODUCTION 33 Fig. 1.8 - Frequenc
Page 45 and 46: INTRODUCTION 35 where 1 1 p1 c k =
Page 47 and 48: INTRODUCTION 37 Fig. 1.12 - Group v
Page 49 and 50: INTRODUCTION 39 Fig. 1.13 - Directi
Page 51 and 52: INTRODUCTION 41 for very simple cas
Page 53 and 54: INNOVATIVE SHM TECHNIQUES BASED ON
Page 63 and 64: EXPERIMENTAL ANALYSIS 53 3. EXPERIM
Page 65 and 66: EXPERIMENTAL ANALYSIS 55 Fig. 3.3 -
Page 67 and 68: EXPERIMENTAL ANALYSIS 57 same point
Page 69 and 70: EXPERIMENTAL ANALYSIS 59 Fig. 3.5 -
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EXPERIMENTAL ANALYSIS 61 Fig. 3.8 -
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EXPERIMENTAL ANALYSIS 63 of the ene
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EXPERIMENTAL ANALYSIS 65 Fig. 3.13
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EXPERIMENTAL ANALYSIS 69 Analysis g
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EXPERIMENTAL ANALYSIS 71 Analysis g
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EXPERIMENTAL ANALYSIS 73 and in spa
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EXPERIMENTAL ANALYSIS 75 and are tr
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EXPERIMENTAL ANALYSIS 77 obtained w
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EXPERIMENTAL ANALYSIS 81 (a) t = 68
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EXPERIMENTAL ANALYSIS 97 The data p
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EXPERIMENTAL ANALYSIS 101 To increa
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EXPERIMENTAL ANALYSIS 103 Both of t
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EXPERIMENTAL ANALYSIS 107 Complete
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FINITE ELEMENT MODELING OF WAVE PRO
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CONCLUSIONS AND FUTURE WORK 131 5.
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CONCLUSIONS AND FUTURE WORK 133 cas
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REFERENCES 135 [6] Rose, J. L. “A
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REFERENCES 137 [20] Ko, J. M., Wong
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REFERENCES 139 [35] J. D. Achenbach
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