A review on machinery diagnostics and prognostics implementing condition-based maintenance

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.

An introduction to structural health monitoring

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

Polymer nanocomposites based on functionalized carbon nanotubes

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.

A review of structural health monitoring literature 1996-2001

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.

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

Time–frequency analysis in gearbox fault detection using the wigner–ville distribution and pattern recognition

Application of the Wavelet Transform to Fault Detection in a Spur Gear

Auxetic honeycombs show in-plane negative Poisson's ratio properties; they expand in all directions when pulled in only one, and contract when compressed. This characteristic is due to the reentrant shape of the honeycomb unit cell. The cell convoluteness gives a geometric stiffening effect that affects the linear elastic properties of the whole cellular solid. In this paper finite element simulations are carried out to calculate the in-plane Poisson's ratio and Young's moduli of re-entrant cell honeycombs for different geometric layout combinations (side cell aspect ratio, relative thickness and internal cell angle) subjected to uniaxial loading. The results show a high sensitivity of the mechanical properties for particular ranges of the geometric cell parameters. An image data detection technique is used to extract displacements and strains from an aramid paper re-entrant honeycomb sample in a tensile test. The comparison between numerical and experimental results shows good agreement.

Numerical and experimental uniaxial loading on in-plane auxetic honeycombs

The work described in this paper is concerned with controlling the strain of the constraining layer of a composite structure in such a way as to enhance the shear generated in the viscoelastic material and hence improve the overall damping of the composite structure. The results have indicated that this concept of active damping produces very effective levels of vibration suppression. In the case of cantilever beams the vibration levels in the first two modes can be almost eliminated when velocity feedback of the beam tip is used. The results show that the addition of active control and passive damping in a single structure combines the advantages of passive damping in the higher modes and active control in the lower modes. In addition active damping as defined in this paper produces a fail-safe mechanism in case of instability occurring in the feedback loop since a considerable level of passive damping is always present.

https://iopscience.iop.org/article/10.1088/0964-1726/4/1/001/pdf

Geoffrey R. Tomlinson

Papers

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

Time–frequency analysis in gearbox fault detection using the wigner–ville distribution and pattern recognition

Application of the Wavelet Transform to Fault Detection in a Spur Gear

Numerical and experimental uniaxial loading on in-plane auxetic honeycombs

Use of active constrained-layer damping for controlling resonant vibration