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E-raamat: Wavelets: Theory and Applications for Manufacturing

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Dec-2010
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781441915450
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Wavelets: Theory and Applications for ManufacturingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 07-Dec-2010
  • Kirjastus: Springer-Verlag New York Inc.
  • Keel: eng
  • ISBN-13: 9781441915450
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Wavelets: Theory and Applications for Manufacturing presents a systematic description of the fundamentals of wavelet transform and its applications. Given the widespread utilization of rotating machines in modern manufacturing and the increasing need for condition-based, as opposed to fix-interval, intelligent maintenance to minimize machine down time and ensure reliable production, it is of critical importance to advance the science base of signal processing in manufacturing. This volume also deals with condition monitoring and health diagnosis of rotating machine components and systems, such as bearings, spindles, and gearboxes, while also:-Providing a comprehensive survey on wavelets specifically related to problems encountered in manufacturing-Discussing the integration of wavelet transforms with other soft computing techniques such as fuzzy logic, for machine defect and severity classification-Showing how to custom design wavelets for improved performance in signal analysisFocusing on wavelet transform as a tool specifically applied and designed for applications in manufacturing, Wavelets: Theory and Applications for Manufacturing presents material appropriate for both academic researchers and practicing engineers working in the field of manufacturing.

With the aim of facilitating signal processing in manufacturing, this book presents a systematic description of the fundamentals on wavelet transform and the ways of applying it to the condition monitoring and health diagnosis of rotating machine components.

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From the reviews:

The book presents the theory and practice of wavelets and shows applicability of the wavelet transform to solve the problems typically encountered in various fields of engineering. The book can be recommended for both researches and practitioners working in the field of manufacturing. (Zygmunt Hasiewicz, Zentralblatt MATH, Vol. 1227, 2012)

1 Signals and Signal Processing in Manufacturing
1(16)
1.1 Classification of Signals
1(4)
1.1.1 Deterministic Signal
1(2)
1.1.2 Nondeterministic Signal
3(2)
1.2 Signals in Manufacturing
5(6)
1.3 Role of Signal Processing for Manufacturing
11(2)
1.4 References
13(4)
2 From Fourier Transform to Wavelet Transform: A Historical Perspective
17(16)
2.1 Fourier Transform
18(3)
2.2 Short-Time Fourier Transform
21(5)
2.3 Wavelet Transform
26(5)
2.4 References
31(2)
3 Continuous Wavelet Transform
33(16)
3.1 Properties of Continuous Wavelet Transform
35(3)
3.1.1 Superposition Property
35(1)
3.1.2 Covariant Under Translation
36(1)
3.1.3 Covariant Under Dilation
36(1)
3.1.4 Moyal Principle
37(1)
3.2 Inverse Continuous Wavelet Transform
38(1)
3.3 Implementation of Continuous Wavelet Transform
39(2)
3.4 Some Commonly Used Wavelets
41(4)
3.4.1 Mexican Hat Wavelets
41(1)
3.4.2 Morlet Wavelet
41(1)
3.4.3 Gaussian Wavelet
42(1)
3.4.4 Frequency B-Spline Wavelet
43(1)
3.4.5 Shannon Wavelet
43(1)
3.4.6 Harmonic Wavelet
44(1)
3.5 CWT of Representative Signals
45(2)
3.5.1 CWT of Sinusoidal Function
45(1)
3.5.2 CWT of Gaussian Pulse Function
46(1)
3.5.3 CWT of Chirp Function
46(1)
3.6 Summary
47(1)
3.7 References
47(2)
4 Discrete Wavelet Transform
49(20)
4.1 Discretization of Scale and Translation Parameters
49(4)
4.2 Multiresolution Analysis and Orthogonal Wavelet Transform
53(3)
4.2.1 Multiresolution Analysis
53(2)
4.2.2 Orthogonal Wavelet Transform
55(1)
4.3 Dual-Scale Equation and Multiresolution Filters
56(2)
4.4 The Mallat Algorithm
58(2)
4.5 Commonly Used Base Wavelets
60(5)
4.5.1 Haar Wavelet
61(1)
4.5.2 Daubechies Wavelet
61(1)
4.5.3 Coiflet Wavelet
62(1)
4.5.4 Symlet Wavelet
63(1)
4.5.5 Biorthogonal and Reverse Biorthogonal Wavelets
63(2)
4.5.6 Meyer Wavelet
65(1)
4.6 Application of Discrete Wavelet Transform
65(3)
4.7 Summary
68(1)
4.8 References
68(1)
5 Wavelet Packet Transform
69(14)
5.1 Theoretical Basis of Wavelet Packet
69(4)
5.1.1 Definition
69(3)
5.1.2 Wavelet Packet Property
72(1)
5.2 Recursive Algorithm
73(1)
5.3 FFT-Based Harmonic Wavelet Packet Transform
74(4)
5.3.1 Harmonic Wavelet Transform
74(1)
5.3.2 Harmonic Wavelet Packet Algorithm
75(3)
5.4 Application of Wavelet Packet Transform
78(1)
5.4.1 Time-Frequency Analysis
78(1)
5.4.2 Wavelet Packet for Denoising
79(1)
5.5 Summary
79(1)
5.6 References
80(3)
6 Wavelet-Based Multiscale Enveloping
83(20)
6.1 Signal Enveloping Through Hilbert Transform
83(3)
6.2 Multiscale Enveloping Using Complex-Valued Wavelet
86(1)
6.3 Application of Multiscale Enveloping
87(12)
6.3.1 Ultrasonic Pulse Differentiation for Pressure Measurement in Injection Molding
87(6)
6.3.2 Bearing Defect Diagnosis in Rotary Machine
93(6)
6.4 Summary
99(1)
6.5 References
100(3)
7 Wavelet Integrated with Fourier Transform: A Unified Technique
103(22)
7.1 Generalized Signal Transformation Frame
103(6)
7.1.1 Fourier Transform in the Generalized Frame
106(1)
7.1.2 Wavelet Transform in the Generalized Frame
107(2)
7.2 Wavelet Transform with Spectral Postprocessing
109(4)
7.2.1 Fourier Transform of the Measure Function
110(2)
7.2.2 Fourier Transform of Wavelet-Extracted Data Set
112(1)
7.3 Application to Bearing Defect Diagnosis
113(11)
7.3.1 Effectiveness in Defect Feature Extraction
115(3)
7.3.2 Selection of Decomposition Level
118(2)
7.3.3 Effect of Bearing Operation Conditions
120(4)
7.4 Summary
124(1)
7.5 References
124(1)
8 Wavelet Packet-Transform for Defect Severity Classification
125(24)
8.1 Subband Feature Extraction
125(3)
8.1.1 Energy Feature
126(1)
8.1.2 Kurtosis
127(1)
8.2 Key Feature Selection
128(6)
8.2.1 Fisher Linear Discriminant Analysis
129(2)
8.2.2 Principal Component Analysis
131(3)
8.3 Neural-Network Classifier
134(2)
8.4 Formulation of WPT-Based Defect Severity Classification
136(1)
8.5 Case Studies
137(9)
8.5.1 Case Study I: Roller Bearing Defect Severity Evaluation
137(5)
8.5.2 Case Study II: Ball Bearing Defect Severity Evaluation
142(4)
8.6 Summary
146(1)
8.7 References
146(3)
9 Local Discriminant Bases for Signal Classification
149(16)
9.1 Dissimilarity Measures
149(4)
9.1.1 Relative Entropy
150(1)
9.1.2 Energy Difference
151(1)
9.1.3 Correlation Index
151(1)
9.1.4 Nonstationarity
152(1)
9.2 Local Disriminant Bases
153(2)
9.3 Case Study
155(3)
9.4 Application to Gearbox Defect Classification
158(4)
9.5 Summary
162(1)
9.6 References
162(3)
10 Selection of Base Wavelet
165(24)
10.1 Overview of Base Wavelet Selection
165(4)
10.1.1 Qualitative Measure
166(2)
10.1.2 Quantitative Measure
168(1)
10.2 Wavelet Selection Criteria
169(7)
10.2.1 Energy and Shannon Entropy
170(2)
10.2.2 Information Theoretic Measure
172(4)
10.3 Numerical Study on Base Wavelet Selection
176(7)
10.3.1 Evaluation Using Real-Valued Wavelets
176(3)
10.3.2 Evaluation Using Complex-Valued Wavelets
179(4)
10.4 Base Wavelet Selection for Bearing Vibration Signal
183(2)
10.5 Summary
185(1)
10.6 Refereneces
186(3)
11 Designing Your Own Wavelet
189(16)
11.1 Overview of Wavelet Design
189(1)
11.2 Construction of an Impulse Wavelet
190(8)
11.3 Impulse Wavelet Application
198(4)
11.4 Summary
202(1)
11.5 References
203(2)
12 Beyond Wavelets
205(16)
12.1 Second Generation Wavelet Transform
205(5)
12.1.1 Theoretical Basis of SGWT
206(2)
12.1.2 Illustration of SGWT in Signal Processing
208(2)
12.2 Ridgelet Transform
210(4)
12.2.1 Theoretical Basis of Ridgelet Transform
210(2)
12.2.2 Application of the Ridgelet Transform
212(2)
12.3 Curvelet Transform
214(4)
12.3.1 Curvelet Transform
214(3)
12.3.2 Application of the Curvelet Transform
217(1)
12.4 Summary
218(1)
12.5 References
219(2)
Index 221
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