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Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications 2nd edition [Hardback]

(University of New South Wales, Australia)
  • Formāts: Hardback, 448 pages, height x width x depth: 254x178x31 mm, weight: 992 g
  • Izdošanas datums: 03-Jun-2021
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119477557
  • ISBN-13: 9781119477556
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  • Bibliotēkām
  • Formāts: Hardback, 448 pages, height x width x depth: 254x178x31 mm, weight: 992 g
  • Izdošanas datums: 03-Jun-2021
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1119477557
  • ISBN-13: 9781119477556
Citas grāmatas par šo tēmu:
Vibration-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 methods of calculating the Teager Kaiser Energy Operator (TKEO) using Hilbert transform methods in the frequency domain.

With a renewed emphasis on the newly realized possibility of making virtual instruments, readers of Vibration-based Condition Monitoring will benefit from the wide variety of new and updated topics, like:





A comprehensive introduction to machine condition monitoring, including maintenance strategies, condition monitoring methods, and an explanation of the basic problem of condition monitoring An exploration of vibration signals from rotating and reciprocating machines, including signal classification and torsional vibrations An examination of basic and newly developed signal processing techniques, including statistical measures, Fourier analysis, Hilbert transform and demodulation, and digital filtering, pointing out the considerable advantages of non-causal processing, since causal processing gives no benefit for condition monitoring A discussion of fault detection, diagnosis and prognosis in rotating and reciprocating machines, in particular new methods using fault simulation, since big data cannot provide sufficient data for late-stage fault development

Perfect for machine manufacturers who want to include a machine monitoring service with their product, Vibration-based Condition Monitoring: Industrial, Automotive and Aerospace Applications will also earn a place in university and research institute libraries where there is an interest in machine condition monitoring and diagnostics.
Foreword xii
About the Author xv
Preface to The Second Edition xvi
About the Companion Website xix
1 Introduction and Background
1(24)
1.1 Introduction
1(1)
1.2 Maintenance Strategies
2(1)
1.3 Condition Monitoring Methods
3(3)
1.3.1 Vibration Analysis
3(2)
1.3.2 Oil Analysis
5(1)
1.3.3 Performance Analysis
6(1)
1.3.4 Thermography
6(1)
1.4 Types and Benefits of Vibration Analysis
6(3)
1.4.1 Benefits Compared with Other Methods
6(1)
1.4.2 Permanent vs Intermittent Monitoring
7(2)
1.5 Vibration Transducers
9(10)
1.5.1 Absolute vs Relative Vibration Measurement
10(1)
1.5.2 Proximity Probes
11(2)
1.5.5 Velocity Transducers
13(1)
1.5.4 Accelerometers
14(4)
1.5.5 Dual Vibration Probes
18(1)
1.5.6 Laser Vibrometers
18(1)
1.6 Torsional Vibration Transducers
19(1)
1.6.1 Shaft Encoders
19(1)
1.6.2 Torsional Laser Vibrometers
20(1)
1.7 Condition Monitoring -- The Basic Problem
20(3)
References
23(2)
2 Vibration Signals from Rotating and Reciprocating Machines
25(38)
2.1 Signal Classification
25(5)
2.1.1 Stationary Deterministic Signals
28(1)
2.1.2 Stationary Random Signals
28(1)
2.1.3 Cyclostationary Signals
29(1)
2.1.4 Cyclo-non-stationary Signals
30(1)
2.2 Signals Generated by Rotating Machines
30(25)
2.2.1 Low Shaft Orders and Subharmonics
30(9)
2.2.2 Vibrations from Gears
39(7)
2.2.3 Rolling Element Bearings
46(4)
2.2.4 Bladed Machines
50(1)
2.2.5 Electrical Machines
51(4)
2.3 Signals Generated by Reciprocating Machines
55(5)
2.3.1 Time-Frequency Diagrams
55(4)
2.3.2 Torsional Vibrations
59(1)
References
60(3)
3 Basic Signal Processing Techniques
63(60)
3.1 Statistical Measures
63(4)
3.1.1 Probability and Probability Density
63(1)
3.1.2 Moments and Cumulants
64(3)
3.2 Fourier Analysis
67(26)
3.2.1 Fourier Series
67(1)
3.2.2 Fourier Integral Transform
68(2)
3.2.3 Sampled Time Signals
70(1)
3.2.4 The Discrete Fourier Transform (DFT)
70(2)
3.2.5 The Fast Fourier Transform (FFT)
72(1)
3.2.6 Convolution and the Convolution Theorem
73(10)
3.2.7 Zoom FFT
83(1)
3.2.8 Practical FFT Analysis
84(9)
3.3 Hilbert Transform and Demodulation
93(8)
3.3.1 Hilbert Transform
93(1)
3.3.2 Demodulation
94(7)
3.4 Digital Filtering
101(3)
3.4.1 Realisation of Digital Filters
102(1)
3.4.2 Comparison of Digital Filtering with FFT Processing
103(1)
3.5 Time/Frequency Analysis
104(7)
3.5.1 The Short Time Fourier Transform (STFT)
104(1)
3.5.2 The Wigner-Ville Distribution
104(1)
3.5.3 Wavelet Analysis
105(3)
3.5.4 Empirical Mode Decomposition
108(3)
3.6 Cyclostationary Analysis and Spectral Correlation
111(8)
3.6.1 Spectral Correlation
112(2)
3.6.2 Spectral Correlation and Envelope Spectrum
114(1)
3.6.3 Wigner-Ville Spectrum
114(2)
3.6.4 Cyclo-non-stationary Analysis
116(3)
References
119(4)
4 Fault Detection
123(24)
4.1 Introduction
123(1)
4.2 Rotating Machines
123(12)
4.2.1 Vibration Criteria
123(4)
4.2.2 Use of Frequency Spectra
127(1)
4.2.3 CPB Spectrum Comparison
128(7)
4.3 Reciprocating Machines
135(11)
4.3.1 Vibration Criteria for Reciprocating Machines
135(1)
4.3.2 Time/Frequency Diagrams
136(3)
4.3.3 Torsional Vibration
139(7)
References
146(1)
5 Some Special Signal Processing Techniques
147(52)
5.1 Order Tracking
147(16)
5.7.7 Comparison of Methods
147(1)
5.7.2 Computed Order Tracking (COT)
148(3)
5.1.3 Phase Demodulation Based COT
151(5)
5.1.4 COT Over a Wide Speed Range
156(7)
5.2 Determination of Instantaneous Machine Speed
163(14)
5.2.7 Derivative of Instantaneous Phase
163(5)
5.2.2 Teager Kaiser and Other Energy Operators
168(2)
5.2.3 Comparison of Time and Frequency Domain Approaches
170(4)
5.2.4 Other Methods
174(3)
5.3 Deterministic/Random Signal Separation
177(10)
5.3.1 Time Synchronous Averaging
178(2)
5.3.2 Linear Prediction
180(3)
5.3.3 Adaptive Noise Cancellation
183(1)
5.3.4 Self Adaptive Noise Cancellation
183(2)
5.3.5 Discrete/Random Separation (DRS)
185(2)
5.4 Minimum Entropy Deconvolution
187(2)
5.5 Spectral Kurtosis and the Kurtogram
189(8)
5.5.7 Spectral Kurtosis -- Definition and Calculation
190(2)
5.5.2 Toe of SKas a Filter
192(1)
5.5.3 The Kurtogram
193(4)
References
197(2)
6 Cepstrum Analysis Applied to Machine Diagnostics
199(32)
6.1 Cepstrum Terminology and Definitions
199(3)
6.7.7 Brief History of the Cepstrum and Terminology
199(3)
67.2 Cepstrum Types and Definitions
202(3)
6.2 Typical Applications of the Real Cepstrum
205(11)
6.2.7 Practical Considerations with the Cepstrum
205(3)
6.2.2 Detecting and Quantifying Harmonic/Sideband Families
208(6)
6.2.3 Separation of Forcing and Transfer Functions
214(2)
6.3 Modifying Time Signals Using the Real Cepstrum
216(12)
6.3.7 Removing Harmonic/Sideband Families
217(5)
6.3.2 Enhancing/Removing Modal Properties
222(3)
6.3.3 Cepstrum Pre-whitening
225(3)
References
228(3)
7 Diagnostic Techniques for Particular Applications
231(78)
7.1 Harmonic and Sideband Cursors
231(5)
7.1.1 Basic Principles
231(1)
7.7.2 Examples of Cursor Application
232(1)
7.1.3 Combination with Order Tracking
232(4)
7.2 Gear Diagnostics
236(34)
7.2.7 Techniques Based on the TSA
236(2)
7.2.2 Transmission Error as a Diagnostic Tool
238(16)
7.2.3 Cepstrum Analysis for Gear Diagnostics
254(9)
7.2.4 Separation of Spalls and Cracks
263(4)
7.2.5 Diagnostics of Gears with Varying Speed and Load
267(3)
7.3 Rolling Element Bearing Diagnostics
270(25)
7.3.1 Signal Models for Bearing Faults
273(4)
7.3.2 A Semi-Automated Bearing Diagnostic Procedure
277(6)
7.3.3 Alternative Diagnostic Methods for Special Conditions
283(2)
7.3.4 Diagnostics of Bearings with Varying Speed and Load
285(10)
7.4 Reciprocating Machine and IC Engine Diagnostics
295(9)
7.4.1 Time/Frequency Methods
295(2)
7.4.2 Cylinder Pressure Identification
297(6)
7.4.3 Mechanical Fault Identification
303(1)
References
304(5)
8 Fault Simulation
309(46)
8.1 Background and Justification
309(1)
8.2 Simulation of Faults in Gears
310(14)
8.2.1 Lumped Parameter Models of Parallel Gears
310(6)
8.2.2 Separation of Spalls and Cracks
316(4)
8.2.3 Lumped Parameter Models of Planetary Gears
320(2)
8.2.4 Interaction of Faults with Ring and Sun Gears
322(2)
8.3 Simulation of Faults in Bearings
324(14)
8.3.1 Local Faults in LPM Gearbox Model
325(2)
8.3.2 Extended Faults in LPM Gearbox Model
327(2)
8.3.3 Reduced FE Casing Model Combined with LPM Gear Model
329(9)
8.4 Simulation of Faults in Engines
338(16)
8.4.1 Misfire
338(9)
8.4.2 Piston Slap
347(4)
8.4.3 Bearing Knock
351(3)
References
354(1)
9 Fault Trending and Prognostics
355(38)
9.1 Introduction
355(1)
9.2 Trend Analysis
355(17)
9.2.1 Trending of Simple Parameters
356(5)
9.2.2 Trending of `Impulsiveness'
361(3)
9.2.3 Trending of Spall Size in Bearings
364(8)
9.3 Advanced Prognostics
372(15)
9.3.1 Physics-Based Models
372(3)
9.3.2 Data-Driven Models
375(2)
9.3.3 Hybrid Models
377(3)
9.3.4 Simulation-Based Prognostics
380(7)
9.4 Future Developments
387(3)
9.4.1 Advanced Modelling
387(2)
9.4.2 Advances in Data Analytics
389(1)
References
390(3)
Appendix: Exercises and Tutorial Questions
393(30)
Introduction
393(1)
A.1 Introduction and Background
393(1)
A.1.1 Exam Questions
393(1)
A.2 Vibration Signals from Machines
394(2)
A.2.1 Exam Questions
394(2)
A.3 Basic Signal Processing
396(12)
A.3.1 Tutorial and Exam Questions
396(12)
A.4 Fault Detection
408(6)
A.4.1 Tutorial and Exam Questions
408(5)
A.4.2 Assignment
413(1)
A.6 Cepstrum Analysis Applied to Machine Diagnostics
414(1)
A.6.1 Tutorial and Exam Questions
414(1)
A.7 Diagnostic Techniques for Particular Applications
415(7)
A.7.1 Tutorial and Exam Questions
415(3)
A.7.2 Assignments
418(4)
A.9 Prognostics
422(1)
A.9.1 Tutorial and Exam questions
422(1)
Index 423
Robert Bond Randall, is Emeritus Professor in the Mechanical and Manufacturing Engineering Department at the University of New South Wales in Australia. His research focus is on vibration analysis and signal processing applied to machine condition monitoring. He is the Chief Investigator for three Australian Research Council research grants since 2016 alone.