Ebook
Random Data: Analysis and Measurement Procedures, 4th EditionISBN: 9781118210826
640 pages
September 2011

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 multipleinput/singleoutput 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 selfcontained, Random Data, Fourth Edition is an indispensible book for courses on random data analysis theory and applications at the upperundergraduate 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.
Preface to the Third Edition.
Glossary of Symbols.
1 Basic Descriptions and Properties.
1.1 Deterministic Versus Random Data.
1.2 Classifications of Deterministic Data.
1.3 Classifications of Random Data.
1.4 Analysis of Random Data.
2 Linear Physical Systems.
2.1 ConstantParameter Linear Systems.
2.2 Basic Dynamic Characteristics.
2.3 Frequency Response Functions.
2.4 Illustrations of Frequency Response Functions.
2.5 Practical Considerations.
3 Probability Fundamentals.
3.1 One Random Variable.
3.2 Two Random Variables.
3.3 Gaussian (Normal) Distribution.
3.4 Rayleigh Distribution.
3.5 Higher Order Changes of Variables.
4 Statistical Principles.
4.1 Sample Values and Parameter Estimation.
4.2 Important Probability Distribution Functions.
4.3 Sampling Distributions and Illustrations.
4.4 Confidence Intervals.
4.5 Hypothesis Tests.
4.6 Correlation and Regression Procedures.
5 Stationary Random Processes.
5.1 Basic Concepts.
5.2 Spectral Density Functions.
5.3 Ergodic and Gaussian Random Processes.
5.4 Derivative Random Processes.
5.5 Level Crossings and Peak Values.
6 SingleInput/Output Relationships.
6.1 SingleInput/SingleOutput Models.
6.2 SingleInput/MultipleOutput Models.
7 MultipleInput/Output Relationships.
7.1 MultipleInput/SingleOutput Models.
7.2 TwoInput/OneOutput Models.
7.3 General and Conditioned MultipleInput Models.
7.4 Modified Procedure to Solve MultipleInput/SingleOutput Models.
7.5 Matrix Formulas for MultipleInput/MultipleOutput Models.
8 Statistical Errors in Basic Estimates.
8.1 Definition of Errors.
8.2 Mean and Mean Square Value Estimates.
8.3 Probability Density Function Estimates.
8.4 Correlation Function Estimates.
8.5 Autospectral Density Function Estimates.
8.6 Record Length Requirements.
9 Statistical Errors in Advanced Estimates.
9.1 CrossSpectral Density Function Estimates.
9.2 SingleInput/Output Model Estimates.
9.3 MultipleInput/Output Model Estimates.
10 Data Acquisition and Processing.
10.1 Data Acquisition.
10.2 Data Conversion.
10.3 Data Qualification.
10.4 Data Analysis Procedures.
11 Data Analysis.
11.1 Data Preparation.
11.2 Fourier Series and Fast Fourier Transforms.
11.3 Probability Density Functions.
11.4 Autocorrelation Functions.
11.5 Autospectral Density Functions.
11.6 Joint Record Functions.
11.7 MultipleInput/Output Functions.
12 Nonstationary Data Analysis.
12.1 Classes of Nonstationary Data.
12.2 Probability Structure of Nonstationary Data.
12.3 Nonstationary Mean Values.
12.4 Nonstationary Mean Square Values.
12.5 Correlation Structure of Nonstationary Data.
12.6 Spectral Structure of Nonstationary Data.
12.7 Input/Output Relations for Nonstationary Data.
13 The Hilbert Transform.
13.1 Hilbert Transforms for General Records.
13.2 Hilbert Transforms for Correlation Functions.
13.3 Envelope Detection Followed by Correlation.
14 Nonlinear System Analysis.
14.1 ZeroMemory and FiniteMemory Nonlinear Systems.
14.2 SquareLaw and Cubic Nonlinear Models.
14.3 Volterra Nonlinear Models.
14.4 SI/SO Models with Parallel Linear and Nonlinear Systems.
14.5 SI/SO Models with Nonlinear Feedback.
14.6 Recommended Nonlinear Models and Techniques.
14.7 Duffing SDOF Nonlinear System.
14.8 Nonlinear Drift Force Model.
Bibliography.
Appendix A: Statistical Tables.
Appendix B: Definitions for Random Data Analysis.
List of Figures.
List of Tables.
List of Examples.
Answers to Problems in Random Data.
Index.
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.
 New examples with solutions in the text and 10 problems at the end of each chapter. Answers are provided for the first time in the Appendix
 A new chapter on frequency domain techniques
 New material on the analysis of multipleinput/multipleoutput linear models, laser vibrometers, wireless (telemetry) standards, sigmadelta analogtodigital converters
 A complete rewrite of the material on Fast Fourier Transforms (FFTs)
 The latest methods for data acquisition and processing and nonstationary data analysis