Description
Book SynopsisVibration-based Condition Monitoring
Stay up to date on the newest developments in machine condition monitoring with this brand-new resource from an industry leader
The newly revised Second Edition of Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications delivers a thorough update to the most complete discussion of the field of machine condition monitoring. The distinguished author offers readers new sections on diagnostics of variable speed machines, including wind turbines, as well as new material on the application of cepstrum analysis to the separation of forcing functions, structural model properties, and the simulation of machines and faults.
The book provides improved methods of order tracking based on phase demodulation of reference signals and new methods of determining instantaneous machine speed from the vibration response signal. Readers will also benefit from an insightful discussion of new meth
Table of Contents
Chapter 1 Introduction and Background
1.1 Introduction
1.2 Maintenance strategies
1.3 Condition monitoring methods
1.3.1 Vibration analysis
1.3.2 Oil analysis
1.3.3 Performance analysis
1.3.4 Thermography
1.4 Types and benefits of vibration analysis
1.4.1 Benefits compared with other methods
1.4.2 Permanent vs intermittent monitoring
1.5 Vibration transducers
1.5.1 Absolute vs relative vibration measurement
1.5.2 Proximity probes
1.5.3 Velocity transducers
1.5.4 Accelerometers
1.5.5 Dual vibration probes
1.5.6 Laser vibrometers
1.6 Torsional vibration transducers
1.6.1 Shaft encoders
1.6.2 Torsional laser vibrometers
1.7 Condition monitoring – the basic problem
References
Chapter 2 Vibration Signals from Rotating and Reciprocating Machines
2.1 Signal classification
2.1.1 Stationary deterministic signals
2.1.2 Stationary random signals
2.1.3 Cyclostationary signals
2.1.4 Cyclo-non-stationary signals
2.2 Signals generated by rotating machines
2.2.1 Low shaft orders and subharmonics
2.2.2 Vibrations from gears
2.2.3 Rolling element bearings
2.2.4 Bladed machines
2.2.5 Electrical machines
2.3 Signals generated by reciprocating machines
2.3.1 Time-frequency diagrams
2.3.2 Torsional vibrations
References
Chapter 3 Basic signal processing techniques
3.1 Statistical measures
3.1.1 Probability and probability density
3.1.2 Moments and cumulants
3.2 Fourier analysis
3.2.1 Fourier series
3.2.2 Fourier integral transform
3.2.3 Sampled time signals
3.2.4 The discrete Fourier transform (DFT)
3.2.5 The fast Fourier transform (FFT)
3.2.6 Convolution and the convolution theorem
3.2.7 Zoom FFT
3.2.8 Practical FFT analysis and scaling
3.3 Hilbert transform and demodulation
3.3.1 Hilbert transform
3.3.2 Demodulation
3.4 Digital filtering
3.4.1 Realisation of digital filters
3.4.2 Comparison of digital filtering with FFT processing
3.5 Time/frequency analysis
3.5.1 The short time Fourier transform (STFT)
3.5.2 The Wigner-Ville distribution
3.5.3 Wavelet analysis
3.5.4 Empirical mode decomposition
3.6 Cyclostationary analysis and spectral correlation
3.6.1 Spectral correlation
3.6.2 Spectral correlation and envelope spectrum
3.6.3 Wigner-Ville spectrum
3.6.4 Cyclo-non-stationary analysis
References
Chapter 4 Fault Detection
4.1 Introduction
4.2 Rotating machines
4.2.1 Vibration criteria
4.2.2 Use of frequency spectra
4.2.3 CPB spectrum comparison
4.3 Reciprocating machines
4.3.1 Vibration criteria for reciprocating machines
4.3.2 Time/frequency diagrams
4.3.3 Torsional vibration
References
Chapter 5 Some special signal processing techniques
5.1 Order tracking
5.1.1 Comparison of methods
5.1.2 Computed order tracking(COT)
5.1.3 Phase demodulation based COT
5.1.4 COT over a wide speed range
5.2 Determination of instantaneous machine speed
5.2.1 Derivative of instantaneous phase
5.2.2 Teager Kaiser and other energy operators
5.2.3 Comparison of time and frequency domain approaches
5.2.4 Other methods
5.3 Deterministic/random signal separation
5.3.1 Time synchronous averaging
5.3.2 Linear prediction
5.3.3 Adaptive noise cancellation
5.3.4 Self adaptive noise cancellation
5.3.5 Discrete/random separation (DRS)
5.4 Minimum entropy deconvolution
5.5 Spectral kurtosis and the kurtogram
5.5.1 Spectral kurtosis – definition and calculation
5.5.2 Use of SK as a filter
5.5.3 The kurtogram
References
Chapter 6 Cepstrum analysis applied to machine diagnostics
6.1 Cepstrum terminology and definitions
6.1.1 Brief history of the cepstrum and terminology
6.1.2 Cepstrum types and definitions
6.2 Applications of the real cepstrum
6.2.1 Practical considerations with the cepstrum
6.2.2 Detecting and quantifying harmonic/sideband families
6.2.3 Separation of forcing and transfer functions
6.3 Modifying time signals using the real cepstrum
6.3.1 Removing harmonic/sideband families
6.3.2 Enhancing/removing modal properties
6.3.3 Cepstrum pre-whitening
References
Chapter 7 Diagnostic Techniques for particular applications
7.1 Harmonic and sideband cursors
7.1.1 Basic principles
7.1.2 Examples of cursor application
7.1.3 Combination with order tracking
7.2 Gear diagnostics
7.2.1 Techniques based on the TSA
7.2.2 Transmission error as a diagnostic tool
7.2.3 Cepstrum analysis for gear diagnostics
7.2.4 Separation of spalls and cracks
7.2.5 Diagnostics of gears with varying speed and load
7.3 Rolling element bearing diagnostics
7.3.1 Signal models for bearing faults
7.3.2 A semi-automated bearing diagnostic procedure
7.3.3 Alternative diagnostic methods for special conditions
7.3.4 Diagnostics of bearings with varying speed and load
7.4 Reciprocating machine and IC engine diagnostics
7.4.1 Time/frequency methods
7.4.2 Cylinder pressure identification
7.4.3 Mechanical fault identification
References
Chapter 8 Fault simulation
8.1 Background and justification
8.2 Simulation of faults in gears
8.2.1 Lumped parameter models of parallel gears
8.2.2 Separation of spalls and cracks
8.2.3 Lumped parameter models of planetary gears
8.2.4 Interaction of faults with ring and sun gears
8.3 Simulation of faults in bearings
8.3.1 Local faults in LPM gearbox model
8.3.2 Extended faults in LPM gearbox model
8.3.3 Reduced FE casing model combined with LPM gear model
8.4 Simulation of faults in engines
8.4.1 Misfire
8.4.2 Piston slap
8.4.3 Bearing knock
References
Chapter 9 Fault trending and prognostics
9.1 Introduction
9.2 Trend analysis
9.2.1 Trending of simple parameters
9.2.2 Trending of “impulsiveness”
9.2.3 Trending of spall size in bearings
9.3 Advanced prognostics
9.3.1 Physics-based models
9.3.2 Data-driven models
9.3.3 Hybrid models
9.3.4 Simulation-based prognostics
9.4 Future developments
9.4.1 Advanced modelling
9.4.2 Advances in data analytics
References
