5.5
Conclusion............................. 77
6
Feature
Extraction
79
6.1
Introduction............................ 79
6.2
ProgressionAnalysis
....................... 79
6.2.1
BasicProgressionTypes
................. 80
6.2.2
ProgressionModeling................... 82
6.3
LinearProgressionAnalysis
................... 89
6.3.1
Segmentation
....................... 90
6.3.2
SegmentConcatenation
................. 90
6.3.3
TrendAnalysis
...................... 91
6.4
SigmoidProgressionAnalysis
.................. 93
6.4.1
SigmoidSeries....................... 94
6.4.2
EstimationMethods
................... 95
6.4.3
TrendAnalysis
......................101
6.5
Non-Parametric
Progression
Analysis
. . . . . . . . . . . . . . 103
6.5.1
OutlierSeparation
....................103
6.5.2
EdgeSeparation......................104
6.5.3
RandomNoiseSeparation
................105
6.5.4
TrendAnalysis
......................107
6.6
Calibration
............................109
6.6.1
LinearProgressionAnalysis
...............110
6.6.2
Sigmoid
Progression
Analysis
. . . . . . . . . . . . . . 110
6.6.3
Non-Parametric
Progression
Analysis
. . . . . . . . . . 111
6.7
Conclusion.............................112
7
Fault
Detection
115
7.1
Introduction............................115
7.2
Classi.cation
...........................115
7.3
Performance............................118
7.4
Conclusion.............................125
8
Conclusion
127
8.1
General
..............................127
8.2
UserFriendliness
.........................127
8.3
Reliability
.............................128
8.4
ForwardPerspectives
.......................129
A
Mathematical
Notations
131
A.1
MovingMedian
..........................131
A.2
WindowedRMS..........................131
A.3
Wavelets..............................131
A.3.1
Continuous
Wavelet
Transform
. . . . . . . . . . . . . 132
A.3.2
DiscreteWaveletTransform
...............132
A.3.3
Stationary
Wavelet
Transform
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