A neural network approach to fluid quantity measurement in dynamic environments:
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
London [u.a.]
Springer
2012
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | XI, 138 S. graph. Darst. |
| ISBN: | 9781447140597 |
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MARC
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| 245 | 1 | 0 | |a A neural network approach to fluid quantity measurement in dynamic environments |c Edin Terzic ... |
| 264 | 1 | |a London [u.a.] |b Springer |c 2012 | |
| 300 | |a XI, 138 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Ingenieurwissenschaften | |
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Hydraulic engineering | |
| 650 | 4 | |a Computational Intelligence | |
| 650 | 4 | |a Engineering Fluid Dynamics | |
| 650 | 4 | |a Measurement Science and Instrumentation | |
| 700 | 1 | |a Terzic, Edin |e Sonstige |4 oth | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-4471-4060-3 |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025119575&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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| 999 | |a oai:aleph.bib-bvb.de:BVB01-025119575 | ||
Record in the Search Index
| _version_ | 1804149266257543168 |
|---|---|
| adam_text | Contents
1
Introduction
........................................ 1
1.1
Overview
....................................... ]
1.2
Background
..................................... 1
1.3
Aims and Objectives
............................... 6
1.4
Methodology and Approach
.......................... 7
1.5
Outline of the Thesis
............................... 7
References
.......................................... 8
2
Capacitive
Sensing Technology
........................... 11
2.1
Overview
....................................... 11
2.2
Characteristics of Capacitors
.......................... 11
2.2.1
Overview
.................................. 11
2.2.2
A Capacitor
................................ 11
2.2.3
Capacitance
................................ 12
2.2.4
Capacitance in Parallel and Series Circuits
........... 14
2.2.5
Dielectric Constant
........................... 15
2.2.6
Dielectric Strength
........................... 15
2.3
Capacitive
Sensor Applications
........................ 16
2.3.1
Overview
.................................. 16
2.3.2
Proximity Sensing
............................ 17
2.3.3
Position Sensing
............................. 18
2.3.4
Humidity Sensing
............................ 19
2.3.5
Tilt Sensing
................................ 19
2.4
Capacitors in Level Sensing
.......................... 19
2.4.1
Overview
.................................. 9
2.4.2
Sensing Electrodes
........................... 20
2.4.3
Conducting and Non-Conducting Liquids
............ 24
2.5
Effects of Dynamic Environment
.......................
25
Contents
2.5.1
Overview
.................................. 25
2.5.2
Effects of Temperature Variations
................. 25
2.5.3
Effects of Contamination
....................... 26
2.5.4
Influence of Other Factors
...................... 28
2.6
Effects of Liquid Sloshing
........................... 29
2.6.1
Overview
.................................. 29
2.6.2
Slosh Compensation by Dampening Methods
......... 29
2.6.3
Tilt Sensor
................................. 30
2.6.4
Averaging Methods
........................... 32
2.7
Summary
....................................... 34
References
.......................................... 35
Fluid Level Sensing Using Artificial Neural Networks
.......... 39
3.1
Overview
....................................... 39
3.2
Signal Processing and Classification
.................... 39
3.2.1
Overview
.................................. 39
3.2.2
Data Collection
.............................. 39
3.2.3
Signal Filtration
............................. 40
3.2.4
Feature Extraction
............................ 41
3.2.5
Signal Classification
.......................... 44
3.3
Artificial Neural Networks
........................... 45
3.3.1
Neuron Model
.............................. 45
3.3.2
Transfer Function
............................ 47
3.3.3
Perceptron
................................. 48
3.4
Neural Network Architectures
......................... 49
3.4.1
Overview
.................................. 49
3.4.2
Network Layers
............................. 49
3.4.3
Network Topologies
.......................... 49
3.5
Training Principles
................................ 52
3.5.1
Overview
.................................. 52
3.5.2
Supervised Learning
.......................... 52
3.5.3
Unsupervised Learning
........................ 53
3.6
Neural Networks in Dynamic Environments
............... 53
3.6. ]
Overview
.................................. 53
3.7
Temperature Compensation with Neural Networks
.......... 53
References
.......................................... 54
Methodology
........................................ 57
4.1
Overview
....................................... 57
4.2
Capacitive
Sensor-Based Level Sensing
.................. 57
4.2.1
Capacitive
Sensor Signal
....................... 57
4.2.2
Sensor Response Under Slosh Conditions
........... 58
4.3
Design of Methodology
............................. 59
4.4
Feature Selection and Reduction
....................... 61
Contents vji
4.5 Signal Filtration................................ 53
4.6
Influential Factors Analysis
.......................... 66
References
......................................... 67
5
Experimentation
..................................... 69
5.1
Overview
....................................... 69
5.2
Methodology
..................................... 69
5.3
Data Collection and Processing Methodology
.............. 72
5.4
Apparatus and Equipment used in Experimental Programs
..... 73
5.4.1
Capacitive
Level Sensor
....................... 73
5.4.2
Fuel Tank
................................. 75
5.4.3
Linear Actuator
.............................. 75
5.4.4
Heater
.................................... 76
5.4.5
Arizona Dust
............................... 76
5.4.6
Signal Acquisition Card
........................ 78
5.5
Experiment Set A: Study of the Influential Factors
.......... 78
5.5.1
Overview
.................................. 78
5.5.2
Factorial Design
............................. 79
5.5.3
Experimental Setup
........................... 80
5.6
Experiment Set B: Performance Estimation of Static
and Dynamic Neural Networks
........................ 81
5.6.1
Overview
.................................. 81
5.6.2
Experimental Setup
........................... 81
5.6.3
BP Network Architecture
....................... 82
5.6.4
Distributed Time-Delay Network Architecture
........ 84
5.6.5
NARX Network Architecture
.................... 85
5.7
Experiment Set C: Performance Estimation Using
Signal Enhancement
................................ 86
5.7.1
Overview
.................................. 86
5.7.2
Backpropagation Network Architecture
............. 87
5.7.3
Experimental Setup
........................... 88
5.8
Neural Network Data Processing
....................... 90
5.8.1
Network Initialization
......................... 92
5.8.2
Raw Signal Data
............................. 92
5.8.3
Filtration
.................................. 92
5.8.4
Feature Extraction
............................ 93
5.8.5
Network Training
............................ 93
5.8.6
Network Validation
........................... 93
References
.......................................... 94
6
Results
............................................. 95
6.1
Overview
....................................... 95
6.2
Experiment Set A
................................. 95
v¡ü
Contents
6.2.1
Main Effects Plot
............................ 95
6.2.2
Interaction Plots
............................. 96
6.2.3
Summary
.................................. 97
6.3
Experiment Set
В
................................. 98
6.3.1
Frequency Coefficients
........................ 99
6.3.2
Backpropagation Network
...................... 99
6.3.3
Distributed Time-Delay Network
................. 99
6.3.4
NARX Neural Network
........................ 99
6.3.5
Summary
.................................. 100
6.4
Experiment Set
С
................................. 102
6.4.1
Raw
Capacitive
Sensor Signals
................... 102
6.4.2
Selection of Optimal Preprocessing Parameters
(Experiment Set Cl)
.......................... 103
6.4.3
Selection of Optimal Signal Smoothing Parameters
(Experiment Set C2)
.......................... 108
6.4.4
Final Validation Results (Experiment Set C3)
........
Ill
6.4.5
Frequency Coefficients
........................ 112
6.4.6
Network Weights
............................ 114
6.4.7
Validation Results
............................ 115
6.4.8
Validation Error
............................. 118
6.4.9
Summary
.................................. 118
7
Discussion
.......................................... 121
7.1
Overview
....................................... 121
7.2
Backpropagation Network Configurations
................. 121
7.3
Selection of Signal Preprocessing Parameters
.............. 122
7.4
Selection of Signal Smoothing Parameters
................ 124
8
Conclusions and Future Work
........................... 129
8.1
Conclusion
...................................... 129
8.2
Future Work
..................................... 131
Appendices
............................................ 133
About the Authors
....................................... 135
Index
.................................... 137
Edin Terztc
■ Jenny
Terzic
·
Romesh Nagarajah
■ Muhammad Aiamgir
A Neural Network Approach to Fluid Quantity Measurement
in Dynamic Environments
Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain
in dynamic environments. The measurement system described uses a single-lube
capacitive
sensor to obtain an instantaneous level reading of the fluid surface, thereby
accurately determining the fluid quantity in the presence of slosh. A neural network
based classification technique has been applied to predict the actual quantity of the fluid
contained in a tank under sloshing conditions.
In A neural network approach to fluid quantity measurement in dynamic environments,
effects of temperature variations and contamination on the
capacitive
sensor are
discussed, and the authors propose that these effects can also be eliminated with the
proposed neural network based classification system. To examine the performance of the
classification system, many field trials were carried
olii
on a running vehicle at various
tank volume levels that range from
51.
to so
L
The effectiveness of signal enhancement
on the neural network based signal classification system is also investigated. Results
obtained from the investigation are compared with traditionally used statistical averaging
methods, and proses that the neural network based measurement system can produce
highly accurate fluid quantity measurements in a dynamic environment. In this case,
a capacitive
sensor was used to demonstrate this methodology is valid for all types of
electronic sensors.
lhe
approach demonstrated in
Λ
neural network approach to fluid quantity measurement
in dynamic environments can be applied to a wide range of fluid quantity measurement
applications in the automotive, naval and aviation industries to produce accurate fluid
level readings. Students, lecturers, and experts will find the description of current
research about accurate fluid level measurement in dynamic environments using neural
network approach useful.
|
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| dewey-ones | 620 - Engineering and allied operations |
| dewey-raw | 620.1064 |
| dewey-search | 620.1064 |
| dewey-sort | 3620.1064 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Physik Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
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| id | DE-604.BV040263861 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:20:18Z |
| institution | BVB |
| isbn | 9781447140597 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025119575 |
| oclc_num | 801331989 |
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| owner | DE-11 DE-703 |
| owner_facet | DE-11 DE-703 |
| physical | XI, 138 S. graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Springer |
| record_format | marc |
| spelling | A neural network approach to fluid quantity measurement in dynamic environments Edin Terzic ... London [u.a.] Springer 2012 XI, 138 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Ingenieurwissenschaften Engineering Hydraulic engineering Computational Intelligence Engineering Fluid Dynamics Measurement Science and Instrumentation Terzic, Edin Sonstige oth Erscheint auch als Online-Ausgabe 978-1-4471-4060-3 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025119575&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025119575&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | A neural network approach to fluid quantity measurement in dynamic environments Ingenieurwissenschaften Engineering Hydraulic engineering Computational Intelligence Engineering Fluid Dynamics Measurement Science and Instrumentation |
| title | A neural network approach to fluid quantity measurement in dynamic environments |
| title_auth | A neural network approach to fluid quantity measurement in dynamic environments |
| title_exact_search | A neural network approach to fluid quantity measurement in dynamic environments |
| title_full | A neural network approach to fluid quantity measurement in dynamic environments Edin Terzic ... |
| title_fullStr | A neural network approach to fluid quantity measurement in dynamic environments Edin Terzic ... |
| title_full_unstemmed | A neural network approach to fluid quantity measurement in dynamic environments Edin Terzic ... |
| title_short | A neural network approach to fluid quantity measurement in dynamic environments |
| title_sort | a neural network approach to fluid quantity measurement in dynamic environments |
| topic | Ingenieurwissenschaften Engineering Hydraulic engineering Computational Intelligence Engineering Fluid Dynamics Measurement Science and Instrumentation |
| topic_facet | Ingenieurwissenschaften Engineering Hydraulic engineering Computational Intelligence Engineering Fluid Dynamics Measurement Science and Instrumentation |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025119575&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025119575&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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