Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6

Name: Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6 | Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021
Brand: Springer
Price: 287.83 EUR
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Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021

Dario Di Maio Javad Baqersad(Editor)

Springer (Publisher)

1st Edition

Published on 25. August 2021

VIII, 190 pages

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978-3-030-76335-0 (ISBN)

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Content

Intro
Preface
Contents
1 WaveAR: A Real-Time Sensor-Based Augmented Reality Implementation for Operating Deflection Shapes
1.1 Introduction
1.2 Background
1.3 Technical Implementation
1.3.1 Acceleration and Simplification of the Measurement Configuration for Electromechanical Sensors
1.3.2 Universal Data Acquisition Interface
1.3.3 Real-Time Visualization in the Form of an AR Application
1.4 Measurement Setup
1.5 Analysis
1.6 Conclusion
References
2 Full-Field 3D Mode Shape Measurement Using the Multiview Spectral Optical Flow Imaging Method
2.1 Introduction
2.2 Theoretical Background
2.3 Preliminary Experiment
2.4 Conclusions
References
3 Stereophotogrammetry Camera Pose Optimization
3.1 Introduction
3.2 General Stereophotogrammetry Setup
3.3 Stereo Pose Optimization
3.3.1 User Inputs
3.3.2 Setup: Establish Bounding Box
3.3.3 Pose Estimation
3.3.4 Pose Validation
3.3.5 Evaluating Output and Final Pose Selection
3.4 Predicting Observable Pixel Displacements with FEM
3.4.1 Algorithm to Determine Node Visibility from Camera Placements
3.4.2 Definition of Frequency Response Functions to Assess Arbitrary Inputs
3.4.3 Transformation to Modal Coordinates to Acquire Pixel Displacements for Modes of Interest
3.5 Conclusions and Future Work
References
4 Simplified Finite Element Models of Pyramidal Truss Sandwich Panels with Welded Joints for Dynamic Analysis and Their Experimental Validation
4.1 Introduction
4.2 Methodology
4.3 Results of Numerical Analysis
4.4 Experimental Validation
4.5 Conclusion
References
5 Operational Modal Analysis of Rotating Structures Under Ambient Excitation Using Tracking Continuously Scanning Laser Doppler Vibrometry
5.1 Introduction
5.2 Methodology
5.3 Experimental Setup
5.4 OMA Results
5.5 Conclusion
References
6 Delamination Detection in Fiber Metal Laminates Using Ultrasonic Wavefield Imaging
6.1 Introduction
6.2 Literature Review
6.3 Model Setup
6.4 Feature Extraction Process
6.4.1 Detrended Hilbert Envelope Magnitude (DHEM)
6.4.2 Low-Pass Local Phase Derivative (LLPD)
6.5 Qualitative Evaluation of the Feature Performance
6.5.1 Score Metric to Quantify the Feature Performance
6.5.2 Experimental Comparisons
6.6 Conclusion
References
7 One-Dimensional Convolutional Neural Networks for Real-Time Damage Detection of Rotating Machinery
7.1 Introduction and Overview of One-Dimensional CNNs
7.2 Proposed Methodology
7.3 Laboratory Setup for Bearing Tests
7.4 Damage Detection Results
7.5 Performance Evaluation in the Presence of Noisy Data
7.6 Computational Complexity Analysis
7.7 Conclusions
References
8 A Practical Guide to Motion Magnification
8.1 Introduction
8.2 Methodologies
8.2.1 Intensity
8.2.2 Fourier Transform
8.2.3 Complex Steerable Pyramid
8.2.4 Riesz Pyramid
8.3 General Guidelines for Use
8.4 Conclusion
References
9 Squeeze Film Damper Experimental and Numerical Correlation: Test Setup Description and Parameter Identification of Dry System
9.1 Introduction
9.2 Damper Test Stand Description
9.3 Closed-Form Solution
9.4 Experimental Procedure and Results
9.5 Numerical Modeling Procedure and Results
9.5.1 Model Description
9.5.2 Diaphragm Stiffness from Model
9.5.3 Numerical Modal and Harmonic Analysis
9.6 Comparison
9.7 Conclusion
References
10 Full-Field Modal Analysis by Using Digital Image Correlation Technique
10.1 Introduction
10.2 Background
10.2.1 Low Speed Camera for High Frequency Behavior Characterization
10.3 Experimental Setup and Results
10.3.1 Demo Airplane
10.3.2 Car Tire
10.4 Conclusion
References
11 Validating Complex Models Accurately and Without Contact Using Scanning Laser Doppler Vibrometry (SLDV)
11.1 Introduction
11.2 Test Case Scenarios for Vibrometry
11.3 Measurement Principle of a Vibrometer
11.4 Choosing a Vibrometer Configuration
11.5 Optimizing Setup and Test Parameters
11.6 Application Example 1: Modal Test on a Turbine Wheel [1]
11.7 Application Example 2: Traveling Ultrasonic Wave Analysis
11.8 Application Example 3: Dynamic Stress and Strain Characterization [2, 3]
11.9 Conclusion
References
12 Effect of Different Test Setup Configurations on the Identification of Modal Parameters from Digital Image Correlation
12.1 Introduction
12.2 Aliased Acquisition with Low-Speed Camera
12.3 Experimental Setup
12.3.1 Test Item
12.3.2 FRF Testing
12.3.3 Camera Setup/Measurement Chain
12.4 Results
12.4.1 Type of Excitation
12.4.2 Number of Averages
12.4.3 Extended Frequency Range
12.4.4 Effect of Speckling the Surface
12.5 Conclusion
References
13 WaveImage - Order ODS for Rotating Machineries
13.1 Introduction
13.2 Background
13.2.1 Order Analysis
13.2.2 Operating Deflection Shapes
13.2.3 Optical Flow Analysis
13.2.4 Measurement Setup
13.3 Analysis
13.4 Conclusion
References
14 Multi-Level Damage Detection Using Octree Partitioning Algorithm
14.1 Introduction
14.2 The Proposed Algorithm
14.3 Conclusion
References
15 Photogrammetry-Based Experimental Modal Analysis for Plate Structures
15.1 Introduction
15.2 Methodology
15.2.1 Kinematic Relation
15.2.2 Point-Tracking Technique
15.2.3 Photogrammetry-Based EMA
15.3 Experimental Investigation
15.3.1 Experimental Setup
15.3.2 Data Processing
15.3.3 EMA Method Results
15.4 Conclusion
References
16 An Optical Mode Shape-Based Damage Detection Using Convolutional Neural Networks
16.1 Introduction
16.2 Phase-Based Motion Magnification
16.3 Convolutional Neural Networks
16.4 Methodology
16.5 Conclusion
References
17 Full-Field 3D Experimental Modal Analysis from Dynamic Point Clouds Measured Using a Time-of-Flight Imager
17.1 Introduction
17.2 Background
17.3 Analysis
17.4 Conclusions
References
18 Application of a U-Net Convolutional Neural Network to Ultrasonic Wavefield Measurements for Defect Characterization
18.1 Introduction
18.2 Background
18.2.1 Convolutional Neural Networks
18.2.2 Image Segmentation
18.3 Methodology
18.3.1 Project Overview
18.3.2 Dataset Generation
18.3.3 Image Processing
18.3.4 Data Augmentation
18.3.5 CNN Training
18.4 Results and Discussion
18.5 Conclusion
References
19 Application of the RASTAR Method to Continuous Scanning LDV Measurements
19.1 Introduction
19.2 Test Structure, Setup, and Experimental Method
19.3 Results and Analysis
19.4 Conclusions
References

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Rotating Machinery, Optical Methods & Scanning LDV Methods, Volume 6

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