Wieslaw J. Staszewski

Bio: Wieslaw J. Staszewski is an academic researcher from AGH University of Science and Technology. The author has contributed to research in topics: Ultrasonic sensor & Nonlinear acoustics. The author has an hindex of 22, co-authored 38 publications receiving 2571 citations. Previous affiliations of Wieslaw J. Staszewski include University of Sheffield.

Health Monitoring Of Aerospace Structures: Smart Sensor Technologies And Signal Processing

Abstract: List of Contributors. Preface. 1. Introduction (G. Bartelds, J.H. Heida, J. McFeat and C. Boller). 1.1 Health and Usage Monitoring in Aircraft Structures -- Why and How? 1.2 Smart Solution in Aircraft Monitoring. 1.3 End--User Requirements. 1.3.1 Damage Detection. 1.3.2 Load History Monitoring. 1.4 Assessment of Monitoring Technologies. 1.5 Background of Technology Qualification Process. 1.6 Technology Qualification. 1.6.1 Philosophy. 1.6.2 Performance and Operating Requirements. 1.6.3 Qualification Evidence -- Requirements and Provision. 1.6.4 Risks. 1.7 Flight Vehicle Certification. 1.8 Summary. References. 2. Aircraft Structural Health and Usage Monitoring (C. Boller and W.J. Staszewski). 2.1 Introduction. 2.2 Aircraft Structural Damage. 2.3 Ageing Aircraft Problem. 2.4 LifeCycle Cost of Aerospace Structures. 2.4.1 Background. 2.4.2 Example. 2.5 Aircraft Structural Design. 2.5.1 Background. 2.5.2 Aircraft Design Process. 2.6 Damage Monitoring Systems in Aircraft. 2.6.1 Loads Monitoring. 2.6.2 Fatigue Monitoring. 2.6.3 Load Models. 2.6.4 Disadvantages of Current Loads Monitoring Systems. 2.6.5 Damage Monitoring and Inspections. 2.7 Non--Destructive Testing. 2.7.1 Visual Inspection. 2.7.2 Ultrasonic Inspection. 2.7.3 Eddy Current. 2.7.4 Acoustic Emission. 2.7.5 Radiography, Thermography and Shearography. 2.7.6 Summary. 2.8 Structural Health Monitoring. 2.8.1 Vibration and Modal Analysis. 2.8.2 Impact Damage Detection. 2.9 Emerging Monitoring Techniques and Sensor Technologies. 2.9.1 Smart Structures and Materials. 2.9.2 Damage Detection Techniques. 2.9.3 Sensor Technologies. 2.9.4 Intelligent Signal Processing. 2.10 Conclusions. References. 3. Operational Load Monitoring Using Optical Fibre Sensors (P. Foote, M. Breidne, K. Levin, P. Papadopolous, I. Read, M. Signorazzi, L.K. Nilsson, R. Stubbe and A. Claesson). 3.1 Introduction. 3.2 Fibre Optics. 3.2.1 Optical Fibres. 3.2.2 Optical Fibre Sensors. 3.2.3 Fibre Bragg Grating Sensors. 3.3 Sensor Target Specifications. 3.4 Reliability of Fibre Bragg Grating Sensors. 3.4.1 Fibre Strength Degradation. 3.4.2 Grating Decay. 3.4.3 Summary. 3.5 Fibre Coating Technology. 3.5.1 Polyimide Chemistry and Processing. 3.5.2 Polyimide Adhesion to Silica. 3.5.3 Silane Adhesion Promoters. 3.5.4 Experimental Example. 3.5.5 Summary. 3.6 Example of Surface Mounted Operational Load Monitoring Sensor System. 3.6.1 Sensors. 3.6.2 Optical Signal Processor. 3.6.3 Optical Interconnections. 3.7 Optical Fibre Strain Rosette. 3.8 Example of Embedded Optical Impact Detection System. 3.9 Summary. References. 4. Damage Detection Using Stress and Ultrasonic Waves (W.J. Staszewski, C. Boller, S. Grondel, C. Biemans, E. O'Brien, C. Delebarre and G.R. Tomlinson). 4.1 Introduction. 4.2 Acoustic Emission. 4.2.1 Background. 4.2.2 Transducers. 4.2.3 Signal Processing. 4.2.4 Testing and Calibration. 4.3 Ultrasonics. 4.3.1 Background. 4.3.2 Inspection Modes. 4.3.3 Transducers. 4.3.4 Display Modes. 4.4 Acousto--Ultrasonics. 4.5 Guided Wave Ultrasonics. 4.5.1 Background. 4.5.2 Guided Waves. 4.5.3 Lamb Waves. 4.5.4 Monitoring Strategy. 4.6 Piezoelectric Transducers. 4.6.1 Piezoelectricity and Piezoelectric Materials. 4.6.2 Constitutive Equations. 4.6.3 Properties. 4.7 Passive Damage Detection Examples. 4.7.1 Crack Monitoring Using Acoustic Emission. 4.7.2 Impact Damage Detection in Composite Materials. 4.8 Active Damage Detection Examples. 4.8.1 Crack Monitoring in Metallic Structures Using Broadband Acousto--Ultrasonics. 4.8.2 Impact Damage Detection in Composite Structures Using Lamb Waves. 4.9 Summary. References. 5. Signal Processing for Damage Detection (W.J. Staszewski and K. Worden). 5.1 Introduction. 5.2 Data Pre--Processing. 5.2.1 Signal Smoothing. 5.2.2 Signal Smoothing Filters. 5.3 Signal Features for Damage Identification. 5.3.1 Feature Extraction. 5.3.2 Feature Selection. 5.4 Time--Domain Analysis. 5.5 Spectral Analysis. 5.6 Instantaneous Phase and Frequency. 5.7 Time--Frequency Analysis. 5.8 Wavelet Analysis. 5.8.1 Continuous Wavelet Transform. 5.8.2 Discrete Wavelet Transform. 5.9 Dimensionality Reduction Using Linear and Nonlinear Transformation. 5.9.1 Principal Component Analysis. 5.9.2 Sammon Mapping. 5.10 Data Compression Using Wavelets. 5.11 Wavelet--Based Denoising. 5.12 Pattern Recognition for Damage Identification. 5.13 Artificial Neural Networks. 5.13.1 Parallel Processing Paradigm. 5.13.2 The Artificial Neuron. 5.13.3 Multi--Layer Networks. 5.13.4 Multi--Layer Perceptron Neural Networks and Others. 5.13.5 Applications. 5.14 Impact Detection in Structures Using Pattern Recognition. 5.14.1 Detection of Impact Positions. 5.14.2 Detection of Impact Energy. 5.15 Data Fusion. 5.16 Optimised Sensor Distributions. 5.16.1 Informativeness of Sensors. 5.16.2 Optimal Sensor Location. 5.17 Sensor Validation. 5.18 Conclusions. References. 6. Structural Health Monitoring Evaluation Tests (P.A. Lloyd, R. Pressland, J. McFeat, I. Read, P. Foote, J.P. Dupuis, E. O'Brien, L. Reithler, S. Grondel, C. Delebarre, K. Levin, C. Boller, C. Biemans and W.J. Staszewski). 6.1 Introduction. 6.2 Large--Scale Metallic Evaluator. 6.2.1 Lamb Wave Results from Riveted Metallic Specimens. 6.2.2 Acoustic Emission Results from a Full--Scale Fatigue Test. 6.3 Large--Scale Composite Evaluator. 6.3.1 Test Article. 6.3.2 Sensor and Specimen Integration. 6.3.3 Impact Tests. 6.3.4 Damage Detection Results -- Distributed Optical Fibre Sensors. 6.3.5 Damage Detection Results -- Bragg Grating Sensors. 6.3.6 Lamb Wave Damage Detection System. 6.4 Flight Tests. 6.4.1 Flying Test--Bed. 6.4.2 Acoustic Emission Optical Damage Detection System. 6.4.3 Bragg Grating Optical Load Measurement System. 6.4.4 Fibre Optic Load Measurement Rosette System. 6.5 Summary. References. Index.

Acousto-ultrasonic sensing using fiber Bragg gratings

Abstract: This paper describes a fiber-optic system which is able to detect ultrasound in structures. The aim of the sensing system is to monitor structures, in particular aircraft structures, by detecting ultrasonic Lamb waves. This type of monitoring technique has recently become a key topic in structural health monitoring. Most common approaches use piezoceramic devices to launch and receive the ultrasound. A new way of fiber-optic detection of Lamb waves is based on fiber Bragg grating sensors. In addition to the well known advantages of fiber-optic sensors, this new interrogation scheme allows the use of Bragg gratings for both high-resolution strain and high-speed ultrasound detection. The focus of the paper is on the ultrasonic part of the system. The theoretical approach and the implementation into a laboratory set-up are elaborated. Experiments have been carried out to calibrate the system and first results on simple structures show the feasibility of the system for sensing ultrasonic Lamb waves.

Impact damage location in composite structures using optimized sensor triangulation procedure

Abstract: Impacts, which may occur during manufacture, service or maintenance, are the major cause of in-service damage to composite structures. Many investigations have been undertaken in order to assess and locate impact damage. A new method of impact location in composite materials is proposed in this paper. It based on a classical sensor triangulation methodology and combines experimental strain wave velocity analysis with an optimization genetic algorithm procedure. The method is validated on a composite panel with embedded piezoceramic sensors. The paper shows that the new method has potential for effective impact damage location. Strain data from only three piezoceramic sensors provide good impact location results, avoiding learning and modelling difficulties associated with other techniques.

An introduction to structural health monitoring

Abstract: This introduction begins with a brief history of SHM technology development. Recent research has begun to recognise that a productive approach to the Structural Health Monitoring (SHM) problem is to regard it as one of statistical pattern recognition (SPR); a paradigm addressing the problem in such a way is described in detail herein as it forms the basis for the organisation of this book. In the process of providing the historical overview and summarising the SPR paradigm, the subsequent chapters in this book are cited in an effort to show how they fit into this overview of SHM. In the conclusions are stated a number of technical challenges that the authors believe must be addressed if SHM is to gain wider acceptance.

A review of structural health monitoring literature 1996-2001

Abstract: Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This presentation will summarize the outcome of an updated review covering the years 1996 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which addresses four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling for Feature Discrimination. The literature has been reviewed based on how a particular study addresses these four topics. A significant observation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond demonstration problems carried out in laboratory setting.

Structural Health Monitoring: A Machine Learning Perspective

Abstract: This book focuses on structural health monitoring in the context of machine learning. The authors review the technical literature and include case studies. Chapters include: operational evaluation, sensing and data acquisition, introduction to probability and statistics, machine learning and statistical pattern recognition, and data prognosis.