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Random Data: Analysis and Measurement Procedures 4th edition [Kietas viršelis]

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  • Formatas: Hardback, 640 pages, aukštis x plotis x storis: 244x165x38 mm, weight: 1034 g, Drawings: 80 B&W, 0 Color; Screen captures: 10 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 79 B&W, 0 Color
  • Serija: Wiley Series in Probability and Statistics
  • Išleidimo metai: 05-Mar-2010
  • Leidėjas: John Wiley & Sons Inc
  • ISBN-10: 0470248777
  • ISBN-13: 9780470248775
Kitos knygos pagal šią temą:
Random Data: Analysis and Measurement Procedures 4th editionDidesnis paveikslėlis
  • Formatas: Hardback, 640 pages, aukštis x plotis x storis: 244x165x38 mm, weight: 1034 g, Drawings: 80 B&W, 0 Color; Screen captures: 10 B&W, 0 Color; Tables: 0 B&W, 0 Color; Graphs: 79 B&W, 0 Color
  • Serija: Wiley Series in Probability and Statistics
  • Išleidimo metai: 05-Mar-2010
  • Leidėjas: John Wiley & Sons Inc
  • ISBN-10: 0470248777
  • ISBN-13: 9780470248775
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780470248775.html
Raktažodžiai:
A timely update of the classic book on the theory and application of random data analysis First published in 1971, Random Data served as an authoritative book on the analysis of experimental physical data for engineering and scientific applications. This Fourth Edition features coverage of new developments in random data management and analysis procedures that are applicable to a broad range of applied fields, from the aerospace and automotive industries to oceanographic and biomedical research. This new edition continues to maintain a balance of classic theory and novel techniques. The authors expand on the treatment of random data analysis theory, including derivations of key relationships in probability and random process theory. The book remains unique in its practical treatment of nonstationary data analysis and nonlinear system analysis, presenting the latest techniques on modern data acquisition, storage, conversion, and qualification of random data prior to its digital analysis. The Fourth Edition also includes: A new chapter on frequency domain techniques to model and identify nonlinear systems from measured input/output random data New material on the analysis of multiple-input/single-output linear models The latest recommended methods for data acquisition and processing of random data Important mathematical formulas to design experiments and evaluate results of random data analysis and measurement procedures Answers to the problem in each chapter Comprehensive and self-contained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upper-undergraduate and graduate level. It is also an insightful reference for engineers and scientists who use statistical methods to investigate and solve problems with dynamic data. Send Comment
Preface xv
Preface to the Third Edition xvii
Glossary of Symbols xix
Basic Descriptions and Properties
1(24)
Deterministic Versus Random Data
1(2)
Classifications of Deterministic Data
3(5)
Sinusoidal Periodic Data
3(1)
Complex Periodic Data
4(2)
Almost-Periodic Data
6(1)
Transient Nonperiodic Data
7(1)
Classifications of Random Data
8(5)
Stationary Random Data
9(2)
Ergodic Random Data
11(1)
Nonstationary Random Data
12(1)
Stationary Sample Records
12(1)
Analysis of Random Data
13(12)
Basic Descriptive Properties
13(6)
Input/Output Relations
19(2)
Error Analysis Criteria
21(2)
Data Analysis Procedures
23(2)
Linear Physical Systems
25(20)
Constant-Parameter Linear Systems
25(1)
Basic Dynamic Characteristics
26(2)
Frequency Response Functions
28(2)
Illustrations of Frequency Response Functions
30(11)
Mechanical Systems
30(9)
Electrical Systems
39(2)
Other Systems
41(1)
Practical Considerations
41(4)
Probability Fundamentals
45(34)
One Random Variable
45(9)
Probability Density and Distribution Functions
46(3)
Expected Values
49(1)
Change of Variables
50(2)
Moment-Generating and Characteristic Functions
52(1)
Chebyshev's Inequality
53(1)
Two Random Variables
54(5)
Expected Values and Correlation Coefficient
55(1)
Distribution for Sum of Two Random Variables
56(1)
Joint Moment-Generating and Characteristic Functions
57(2)
Gaussian (Normal) Distribution
59(8)
Central Limit Theorem
60(2)
Joint Gaussian (Normal) Distribution
62(1)
Moment-Generating and Characteristic Functions
63(1)
N-Dimensional Gaussian (Normal) Distribution
64(3)
Rayleigh Distribution
67(5)
Distribution of Envelope and Phase for Narrow Bandwidth Data
67(4)
Distribution of Output Record for Narrow Bandwidth Data
71(1)
Higher Order Changes of Variables
72(7)
Statistical Principles
79(30)
Sample Values and Parameter Estimation
79(3)
Important Probability Distribution Functions
82(3)
Gaussian (Normal) Distribution
82(1)
Chi-Square Distribution
83(1)
The t Distribution
84(1)
The F Distribution
84(1)
Sampling Distributions and Illustrations
85(3)
Distribution of Sample Mean with Known Variance
85(1)
Distribution of Sample Variance
86(1)
Distribution of Sample Mean with Unknown Variance
87(1)
Distribution of Ratio of Two Sample Variances
87(1)
Confidence Intervals
88(3)
Hypothesis Tests
91(8)
Chi-Square Goodness-of-Fit Test
94(2)
Nonparametric Trend Test
96(3)
Correlation and Regression Procedures
99(10)
Linear Correlation Analysis
99(3)
Linear Regression Analysis
102(7)
Stationary Random Processes
109(64)
Basic Concepts
109(9)
Correlation (Covariance) Functions
111(2)
Examples of Autocorrelation Functions
113(2)
Correlation Coefficient Functions
115(1)
Cross-Correlation Function for Time Delay
116(2)
Spectral Density Functions
118(24)
Spectra via Correlation Functions
118(8)
Spectra via Finite Fourier Transforms
126(3)
Spectra via Filtering-Squaring-Averaging
129(3)
Wavenumber Spectra
132(2)
Coherence Functions
134(1)
Cross-Spectrum for Time Delay
135(2)
Location of Peak Value
137(1)
Uncertainty Relation
138(2)
Uncertainty Principle and Schwartz Inequality
140(2)
Ergodic and Gaussian Random Processes
142(9)
Ergodic Random Processes
142(3)
Sufficient Condition for Ergodicity
145(2)
Gaussian Random Processes
147(2)
Linear Transformations of Random Processes
149(2)
Derivative Random Processes
151(4)
Correlation Functions
151(3)
Spectral Density Functions
154(1)
Level Crossings and Peak Values
155(18)
Expected Number of Level Crossings per Unit Time
155(4)
Peak Probability Functions for Narrow Bandwidth Data
159(2)
Expected Number and Spacing of Positive Peaks
161(1)
Peak Probability Functions for Wide Bandwidth Data
162(2)
Derivations
164(9)
Single-Input/Output Relationships
173(28)
Single-Input/Single-Output Models
173(17)
Correlation and Spectral Relations
173(7)
Ordinary Coherence Functions
180(3)
Models with Extraneous Noise
183(4)
Optimum Frequency Response Functions
187(3)
Single-Input/Multiple-Output Models
190(11)
Single-Input/Two-Output Model
191(1)
Single-Input/Multiple-Output Model
192(2)
Removal of Extraneous Noise
194(7)
Multiple-Input/Output Relationships
201(48)
Multiple-Input/Single-Output Models
201(6)
General Relationships
202(3)
General Case of Arbitrary Inputs
205(1)
Special Case of Mutually Uncorrelated Inputs
206(1)
Two-Input/One-Output Models
207(14)
Basic Relationships
207(3)
Optimum Frequency Response Functions
210(2)
Ordinary and Multiple Coherence Functions
212(1)
Conditioned Spectral Density Functions
213(6)
Partial Coherence Functions
219(2)
General and Conditioned Multiple-Input Models
221(11)
Conditioned Fourier Transforms
223(1)
Conditioned Spectral Density Functions
224(1)
Optimum Systems for Conditioned Inputs
225(1)
Algorithm for Conditioned Spectra
226(3)
Optimum Systems for Original Inputs
229(2)
Partial and Multiple Coherence Functions
231(1)
Modified Procedure to Solve Multiple-Input/Single-Output Models
232(5)
Three-Input/Single-Output Models
234(1)
Formulas for Three-Input/Single-Output Models
235(2)
Matrix Formulas for Multiple-Input/Multiple-Output Models
237(12)
Multiple-Input/Multiple-Output Model
238(3)
Multiple-Input/Single-Output Model
241(2)
Model with Output Noise
243(2)
Single-Input/Single-Output Model
245(4)
Statistical Errors in Basic Estimates
249(40)
Definition of Errors
249(3)
Mean and Mean Square Value Estimates
252(9)
Mean Value Estimates
252(4)
Mean Square Value Estimates
256(4)
Variance Estimates
260(1)
Probability Density Function Estimates
261(5)
Bias of the Estimate
263(1)
Variance of the Estimate
264(1)
Normalized rms Error
265(1)
Joint Probability Density Function Estimates
265(1)
Correlation Function Estimates
266(7)
Bandwidth-Limited Gaussian White Noise
269(1)
Noise-to-Signal Considerations
270(1)
Location Estimates of Peak Correlation Values
271(2)
Autospectral Density Function Estimates
273(11)
Bias of the Estimate
274(4)
Variance of the Estimate
278(1)
Normalized rms Error
278(2)
Estimates from Finite Fourier Transforms
280(2)
Test for Equivalence of Autospectra
282(2)
Record Length Requirements
284(5)
Statistical Errors in Advanced Estimates
289(28)
Cross-Spectral Density Function Estimates
289(9)
Variance Formulas
292(1)
Covariance Formulas
293(4)
Phase Angle Estimates
297(1)
Single-Input/Output Model Estimates
298(14)
Bias in Frequency Response Function Estimates
300(3)
Coherent Output Spectrum Estimates
303(2)
Coherence Function Estimates
305(3)
Gain Factor Estimates
308(2)
Phase Factor Estimates
310(2)
Multiple-Input/Output Model Estimates
312(5)
Data Acquisition and Processing
317(42)
Data Acquisition
318(8)
Transducer and Signal Conditioning
318(3)
Data Transmission
321(1)
Calibration
322(2)
Dynamic Range
324(2)
Data Conversion
326(9)
Analog-to-Digital Converters
326(2)
Sampling Theorems for Random Records
328(2)
Sampling Rates and Aliasing Errors
330(3)
Quantization and Other Errors
333(2)
Data Storage
335(1)
Data Qualification
335(14)
Data Classification
336(4)
Data Validation
340(5)
Data Editing
345(4)
Data Analysis Procedures
349(10)
Procedure for Analyzing Individual Records
349(2)
Procedure for Analyzing Multiple Records
351(8)
Data Analysis
359(58)
Data Preparation
359(7)
Data Standardization
360(1)
Trend Removal
361(2)
Digital Filtering
363(3)
Fourier Series and Fast Fourier Transforms
366(13)
Standard Fourier Series Procedure
366(2)
Fast Fourier Transforms
368(6)
Cooley-Tukey Procedure
374(2)
Procedures for Real-Valued Records
376(1)
Further Related Formulas
377(1)
Other Algorithms
378(1)
Probability Density Functions
379(2)
Autocorrelation Functions
381(5)
Autocorrelation Estimates via Direct Computations
381(1)
Autocorrelation Estimates via FFT Computations
381(5)
Autospectral Density Functions
386(18)
Autospectra Estimates by Ensemble Averaging
386(2)
Side-Lobe Leakage Suppression Procedures
388(7)
Recommended Computational Steps for Ensemble-Averaged Estimates
395(1)
Zoom Transform Procedures
396(3)
Autospectra Estimates by Frequency Averaging
399(4)
Other Spectral Analysis Procedures
403(1)
Joint Record Functions
404(4)
Joint Probability Density Functions
404(1)
Cross-Correlation Functions
405(1)
Cross-Spectral Density Functions
406(1)
Frequency Response Functions
407(1)
Unit Impulse Response (Weighting) Functions
408(1)
Ordinary Coherence Functions
408(1)
Multiple-Input/Output Functions
408(9)
Fourier Transforms and Spectral Functions
409(1)
Conditioned Spectral Density Functions
409(2)
Three-Input/Single-Output Models
411(3)
Functions in Modified Procedure
414(3)
Nonstationary Data Analysis
417(56)
Classes of Nonstationary Data
417(2)
Probability Structure of Nonstationary Data
419(3)
Higher Order Probability Functions
420(1)
Time-Averaged Probability Functions
421(1)
Nonstationary Mean Values
422(7)
Independent Samples
424(1)
Correlated Samples
425(2)
Analysis Procedures for Single Records
427(2)
Nonstationary Mean Square Values
429(7)
Independent Samples
429(2)
Correlated Samples
431(1)
Analysis Procedures for Single Records
432(4)
Correlation Structure of Nonstationary Data
436(6)
Double-Time Correlation Functions
436(1)
Alternative Double-Time Correlation Functions
437(2)
Analysis Procedures for Single Records
439(3)
Spectral Structure of Nonstationary Data
442(20)
Double-Frequency Spectral Functions
443(2)
Alternative Double-Frequency Spectral Functions
445(4)
Frequency Time Spectral Functions
449(7)
Analysis Procedures for Single Records
456(6)
Input/Output Relations for Nonstationary Data
462(11)
Nonstationary Input and Time-Varying Linear System
463(1)
Results for Special Cases
464(1)
Frequency-Time Spectral Input/Output Relations
465(2)
Energy Spectral Input/Output Relations
467(6)
The Hilbert Transform
473(32)
Hilbert Transforms for General Records
473(11)
Computation of Hilbert Transforms
476(1)
Examples of Hilbert Transforms
477(1)
Properties of Hilbert Transforms
478(2)
Relation to Physically Realizable Systems
480(4)
Hilbert Transforms for Correlation Functions
484(14)
Correlation and Envelope Definitions
484(2)
Hilbert Transform Relations
486(1)
Analytic Signals for Correlation Functions
486(3)
Nondispersive Propagation Problems
489(6)
Dispersive Propagation Problems
495(3)
Envelope Detection Followed by Correlation
498(7)
Nonlinear System Analysis
505(22)
Zero-Memory and Finite-Memory Nonlinear Systems
505(2)
Square-Law and Cubic Nonlinear Models
507(2)
Volterra Nonlinear Models
509(1)
SI/SO Models with Parallel Linear and Nonlinear Systems
510(2)
SI/SO Models with Nonlinear Feedback
512(2)
Recommended Nonlinear Models and Techniques
514(1)
Duffing SDOF Nonlinear System
515(5)
Analysis for SDOF Linear System
516(2)
Analysis for Duffing SDOF Nonlinear System
518(2)
Nonlinear Drift Force Model
520(7)
Basic Formulas for Proposed Model
521(2)
Spectral Decomposition Problem
523(1)
System Identification Problem
524(3)
Bibliography 527(6)
Appendix A: Statistical Tables 533(12)
Appendix B: Definitions for Random Data Analysis 545(12)
List of Figures 557(8)
List of Tables 565(2)
List of Examples 567(4)
Answers to Problems in Random Data 571(28)
Index 599
JULIUS S. BENDAT, PHD, is President of the J. S. Bendat Company, an independent mathematical consulting firm in Los Angeles, California. An internationally recognized authority in the field, Dr. Bendat has over fifty years of consulting experience in the formulation of mathematical models, the development of statistical error analysis criteria, and the interpretation of engineering results. He is the author of Nonlinear System Techniques and Applications and coauthor of Engineering Applications of Correlation and Spectral Analysis, Second Edition, both published by Wiley.

The late ALLAN G. PIERSOL, PE, was president of Piersol Engineering Company. His consulting career spanned over fifty years and focused on a wide range of topics including the development of machinery condition monitoring techniques and the statistical analysis of all types of mechanical shock, vibration, and acoustic data. A Fellow of the Acoustical Society of America and the Institute of Environmental Sciences and Technology, Piersol is the coauthor of Engineering Applications of Correlation and Spectral Analysis, Second Edition.