Includes bibliographical references (pages 195-204) and index.

Development of new sensors and digital processors has provided opportunity for identification of nonlinear systems. Vibration measurements have become standard for predicting and monitoring machinery in industry. Parameter Identification and Monitoring of Mechanical Systems under Nonlinear Vibration focusses on methods for the identification of nonlinearities in mechanical systems, giving description and examples of practical application. Chapters cover nonlinear dynamics; nonlinear vibrations; signal processing; parameter identification; application of signal processing to mechanical systems; practical experience and industrial applications; and synchronization of nonlinear systems.

Print version record.

Cover; Parameter Identification and Monitoring of Mechanical Systems under Nonlinear Vibration; Copyright; Dedication; Table of contents; List of figures; About the author; Introduction; 1 Linear vibrations; 1.1 Introduction; 1.2 Single degree of freedom; 1.3 Multiple degrees of freedom; 2 Nonlinear vibrations; 2.1 Introduction; 2.2 Jump phenomena; 2.3 Self-excited vibrations; 2.4 References; 3 Signal processing; 3.1 Introduction; 3.2 Convolution theorem; 3.3 Fourier Transform; 3.4 Short Time Fourier Transform (STFT); 3.5 Wavelet Transform; 3.6 Discrete Wavelet Transform; 3.7 Phase diagram.

3.8 Approximate entropy3.9 References; 4 Parameter identification; 4.1 Introduction; 4.2 Time domain analysis; 4.3 Frequency domain analysis; 5 Application of signal processing to mechanical systems; 5.1 Introduction; 5.2 Roller bearings; 5.3 Gears; 5.4 Friction; 6 Practical experience and industrial applications; 6.1 Introduction; 6.2 Test procedure; 6.3 Simulation analysis; 7 Synchronization of nonlinear systems; 7.1 Introduction; 7.2 Synchronization; 7.3 Kuramoto's model; 7.4 Synchronization of nonlinear pendulums; Bibliography; Index.

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