Chapter
Chapter 5. Monitoring low-speed rolling element bearings using acoustic emissions
pp.：
54 – 66
Chapter 6. Condition monitoring of rotodynamic machinery using acoustic emission and fuzzy c-mean clustering technique
pp.：
66 – 74
Chapter 7. Monitoring sliding wear using acoustic emission
pp.：
74 – 84
Chapter 8. Intelligent condition monitoring of bearings in mail processing machines using acoustic emission
pp.：
84 – 92
Chapter 9. Health management system design: development, simulation and cost/benefit optimization
pp.：
92 – 106
Chapter 10. Optimisation of SANC for separating gear and bearing signals
pp.：
106 – 114
Chapter 11. A review of fault detection and isolation (FDI) techniques for control and monitoring systems
pp.：
114 – 120
Chapter 12. A monitoring and diagnostic tool for machinery and power plants, based on chaos theory
pp.：
120 – 128
Chapter 13. Novelty detection using minimum variance features
pp.：
128 – 136
Chapter 14. Intelligent signal analysis and wireless signal transfer for purposes of condition monitoring
pp.：
136 – 144
Chapter 15. Condition monitoring for a car engine using higher order time frequency method
pp.：
144 – 152
Chapter 16. An investigation into the development of a condition monitoring/fault diagnostic system for large reversible Francis type Pump-Turbines
pp.：
152 – 160
Chapter 17. Application of vibration diagnostics and suppression by using the Campbell diagram
pp.：
160 – 170
Chapter 18. A novel signal processing approach to eddy current flaw detection based on wavelet analysis
pp.：
170 – 178
Chapter 19. The wavelet analysis applied for fault detection of an electro-hydraulic servo system
pp.：
178 – 186
Chapter 20. Advanced fault diagnosis by vibration and process parameter analysis
pp.：
186 – 194
Chapter 21. Partially blind source separation of the diagnostic signals with prior knowledge
pp.：
194 – 202
Chapter 22. Comparison of simple multi-attribute rating technique and fuzzy linguistic methods in multi-attribute decision making
pp.：
202 – 210
Chapter 23. Reasoning approaches for fault isolation: a comparison study
pp.：
210 – 218
Chapter 24. Migration to advanced maintenance and monitoring techniques in the process industry
pp.：
218 – 226
Chapter 25. Introducing value-based maintenance
pp.：
226 – 234
Chapter 26. Vibration-based maintenance costs, potential savings and benefits: a case study
pp.：
234 – 244
Chapter 27. Balanced scorecard concept adapted to measure maintenance performance: a case study
pp.：
244 – 252
Chapter 28. Design, development and assessment of maintenance system for building industry in developing countries
pp.：
252 – 260
Chapter 29. Using modeling to predict vibration from a shaft crack
pp.：
260 – 268
Chapter 30. An investigation of abnormal high pitch noise in the Train 2 compressor motor
pp.：
268 – 280
Chapter 31. An approach to the development of condition monitoring for a new machine by example
pp.：
280 – 292
Chapter 32. Condition monitoring and diagnostic engineering – a data fusion approach
pp.：
292 – 300
Chapter 33. Teaching the condition monitoring of machines by understanding
pp.：
300 – 308
Chapter 34. A successful model for academia's support of industry's maintenance and reliability needs
pp.：
308 – 314
Chapter 35. Certification in condition monitoring – development of an international PCN scheme for CM personnel
pp.：
314 – 328
Chapter 36. The exploitation of instantaneous angular speed for condition monitoring of electric motors
pp.：
328 – 336
Chapter 37. Discriminating between rotor asymmetries and time-varying loads in three-phase induction motors
pp.：
336 – 346
Chapter 38. Asymmetrical stator and rotor fault detection using vibration, per-phase current and transient speed analysis
pp.：
346 – 362
Chapter 39. New methods for estimating the excitation force of electric motors in operation
pp.：
362 – 370
Chapter 40. The development of flux monitoring for a novel electric motor
pp.：
370 – 378
Chapter 41. European projects - payback time
pp.：
378 – 384
Chapter 42. The use of the fieldbus network for maintenance data communication
pp.：
384 – 392
Chapter 43. A distributed data processing system for process and Condition monitoring
pp.：
392 – 400
Chapter 44. The physical combination of control and condition monitoring
pp.：
400 – 408
Chapter 45. The design and implementation of a data acquisition and control system using fieldbus technologies
pp.：
408 – 416
Chapter 46. A non-linear technique for diagnosing spur gear tooth fatigue cracks: Volterra kernel approach
pp.：
416 – 428
Chapter 47. Detection of gear failures using wavelet transform and improving its capability by principal component analysis
pp.：
428 – 436
Chapter 48. Dynamic analysis method of fault gear equipment
pp.：
436 – 444
Chapter 49. Diagnosis method of gear drive in eccentricity, wear and spot flaw states
pp.：
444 – 450
Chapter 50. Gear damage detection using oil debris analysis
pp.：
450 – 458
Chapter 51. The generalized vibration spectra (GVS) for gearing condition monitoring
pp.：
458 – 466
Chapter 52. Use of genetic algorithm and artificial neural network for gear condition diagnostics
pp.：
466 – 474
Chapter 53. Fault detection on gearboxes operating under fluctuating load conditions
pp.：
474 – 482
Chapter 54. Detection and location of tooth defect in a two-stage helical gearbox using the smoothed instantaneous power spectrum
pp.：
482 – 490
Chapter 55. Securing the successful adoption of a global information delivery system
pp.：
490 – 498
Chapter 56. Design of a PIC based data acquisition system for process and condition monitoring
pp.：
498 – 506
Chapter 57. Applications of diagnosing of naval gas turbines
pp.：
506 – 512
Chapter 58. Diagnosing of naval gas turbine rotors with the use of vibroacoustic parameters
pp.：
512 – 520
Chapter 59. Computer image analysis of dynamic processes
pp.：
520 – 530
Chapter 60. Inverse method of processing motion blur for vibration monitoring of turbine blade
pp.：
530 – 538
Chapter 61. Artificial neural network performance based on different pre-processing techniques
pp.：
538 – 548
Chapter 62. Fault accommodation for diesel engine sensor system using neural networks
pp.：
548 – 554
Chapter 63. The application of neural networks to vibrational diagnostics for multiple fault conditions
pp.：
554 – 562
Chapter 64. Applying neural networks to intelligent condition monitoring
pp.：
562 – 570
Chapter 65. Data mining in a vibration analysis domain by extracting symbolic rules from RBF neural networks
pp.：
570 – 578
Chapter 66. Application of componential coding in fault detection and diagnosis of rotating plant
pp.：
578 – 588
Chapter 67. Bearing fault detection using adaptive neural networks
pp.：
588 – 594
Chapter 68. Analysis of novelty detection properties of autoassociators
pp.：
594 – 602
Chapter 69. Condition monitoring of a hydraulic system using neural networks and expert systems
pp.：
602 – 610
Chapter 70. Multi-layer neural networks and pattern recognition for pump fault diagnosis
pp.：
610 – 616
Chapter 71. Development of an automated fluorescent dye penetrant inspection system
pp.：
616 – 626
Chapter 72.Non-destructive fault induction in an electro-hydraulic servo system
pp.：
626 – 632
Chapter 73. Identification of continuous industrial processes using subspace system identification methods
pp.：
632 – 642
Chapter 74. Life cycle costing as a global imperative
pp.：
642 – 650
Chapter 75. Six sigma initiatives in the field of COMADEM
pp.：
650 – 658
Chapter 76. Monitoring exhaust valve leaks and misfire in marine diesel engines
pp.：
658 – 666
Chapter 77. Combining vibrations and acoustics for the fault detection of marine diesel engines using neural networks and wavelets
pp.：
666 – 674
Chapter 78. Condition diagnosis of reciprocating machinery using information theory
pp.：
674 – 680
Chapter 79. Experimental results in simultaneous identification of multiple faults in rotor systems
pp.：
680 – 690
Chapter 80. Thermodynamic diagnosis at steam turbines
pp.：
690 – 698
Chapter 81. On-line vibration monitoring for detecting fan blade damage
pp.：
698 – 706
Chapter 82. A hybrid knowledge-based expert system for rotating machinery
pp.：
706 – 714
Chapter 83. Monitoring the integrity of low-speed rotating machines
pp.：
714 – 726
Chapter 84. Detecting and diagnosing faults in variable speed machines
pp.：
726 – 734
Chapter 85. ARMADA CMS – advanced rotating machines diagnostics analysis tool for added service productivity
pp.：
734 – 742
Chapter 86. Condition monitoring and diagnosis of rotating machinery by orthogonal expansion of vibration signal
pp.：
742 – 750
Chapter 87. Comparison of approaches to process and sensor fault detection
pp.：
750 – 758
Chapter 88. The neural network prediction of diesel engine smoke emission from routine engine operating parameters of an operating road vehicle
pp.：
758 – 766
Chapter 89. Early detection of leakage in reciprocating compressor valves using vibration and acoustic continuous wavelet features
pp.：
766 – 774
Chapter 90. Inertial sensors error modelling and data correction for the position measurement of parallel kinematics machines
pp.：
774 – 782
Chapter 91. On-line sensor calibration verification: 'a survey'
pp.：
782 – 798
Chapter 92. The applicability of various indirect monitoring methods to tool condition monitoring in drilling
pp.：
798 – 810
Chapter 93. A palm size vibration visualizing instrument for survey diagnosis by using a hand-held type triaxial pickup
pp.：
810 – 818
Chapter 94. Development of an on-line reactor internals vibration monitoring system (RIDS)
pp.：
818 – 826
Chapter 95. Truncation mechanism in a sequential life testing approach with an underlying two-parameter inverse weibull model
pp.：
826 – 834
Chapter 96. Maintenance functional modelling centred on reliability
pp.：
834 – 842
Chapter 97. An implementation of a model-based approach for an electro-hydraulic servo system
pp.：
842 – 850
Chapter 98. Stochastic Petri net modeling for availability and maintainability analysis
pp.：
850 – 858
Chapter 99. The dynamic modelling of multiple pairs of spur gears in mesh including friction
pp.：
858 – 866
Chapter 100. The modelling of a diesel fuel injection system for the non-intrusive monitoring of its condition
pp.：
866 – 874
Chapter 101. Use of factorial simulation experiment in gearbox vibroacoustic diagnostics
pp.：
874 – 882
Chapter 102. Online fault detection and diagnosis of complex systems based on hybrid component models
pp.：
882 – 890
Chapter 103. Measures of accuracy of model based diagnosis of faults in rotormachinery
pp.：
890 – 898
Chapter 104. Failure analysis and fault simulation of an electrohydraulic servo valve
pp.：
898 – 906
Chapter 105. A multiple condition information sources based maintenance model and associated prototype software development
pp.：
906 – 916
Chapter 106. Plant residual time distribution prediction using expert judgements based condition monitoring information
pp.：
916 – 926
Chapter 107. Optimising complex CBM decisions using hybrid fusion methods
pp.：
926 – 936
Chapter 108. Diagnostics of honeycomb core sandwich panels through modal analysis
pp.：
936 – 942
Chapter 109. Assessment of structural integrity monitoring systems
pp.：
942 – 952
Chapter 110. Experimental validation of the constant level method for identification of nonlinear multi degree of freedom systems
pp.：
952 – 960
Chapter 111. A comparative field study of fibre bragg grating strain sensors and resistive foil gauges for structural integrity monitoring
pp.：
960 – 970
Chapter 112. The application of oil debris monitoring and vibration analysis to monitor wear in spur gears
pp.：
970 – 976
Chapter 113. Identification of non-metallic particulate in lubricant filter debris
pp.：
976 – 984
Chapter 114. Influence of turbine load on vibration pattern and symptom limit value determination procedures
pp.：
984 – 994
Chapter 115. Study on the movement regulation of grinding media of vibration mill by noise testing
pp.：
994 – 1002
Chapter 116. Gas turbine blade and disk crack detection using tosional vibration monitoring: a feasibility study
pp.：
1002 – 1010
Chapter 117. The flow-induced vibration of cylinders in heat exchanger
pp.：
1010 – 17
Condition Monitoring and Diagnostic Engineering Management
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