development of underwater quality and natural gas leak detection system using fuzzy neuro approach...

Development of Underwater Quality and Natural Gas Leak Detection

System using Fuzzy Neuro Approach Image Processing

By: Edgar Caburatan Carrillo II

Thesis Proposal for the degree of

Master of Science in Mechanical Engineering

De La Salle University Manila, Philippines

Natural Gas Pipeline System

1. Introduction

1.1. Background of the Study

Worldwide Natural Gas Production Natural Gas in the Philippines Problem with Natural Gas leaking Existing Technologies of Natural Gas Proposed Solution

1. Introduction

1.1. Background of the Study Worldwide Natural Gas Production¾ of World Energy consumption from Natural gas,liquid and coal by 2040 (USEIA, 2013) Natural Gas in the PhilippinesProjects of Philippine government to transport natural through underground and underwater piping networks include: BATMAN 1, SU-MA (Sucat-Malaya), BATMAN 2, ET LOOP and BATCAVE (DOE, 2014) Problem with Natural Gas leaking economic and environmental risks (TRB, 2004)

Existing Technologies of Natural Gas Existing technology need to be improve either by having a leak detection technology that is cheap and accurate (Murvay & Silea, 2012).

Types of Cracks

1.Orifice Crack

www.senninger.com

2.Line Crack

Types of Cracks

3.Stress Corrosion Cracking

www.met-tech.com

4.Hydrogen Induced Cracking

www.masteel.co.uk

Types of Cracks

5.Stress-Oriented Hydrogen Induced Cracking (SOHIC)www.corrosioncontrol.net

6.Lapspmpaspeakingofprecision.com

Types of Cracks

7.Hook Crackswww.china-weldnet.com

8. Fatigue Crackswww.azom.com

Types of Cracks

9. Narrow Axial External Corrosion (NAEC)nainamania.wordpress.com

Flow of Gases in leaks

Turbulent Flow

Laminar Flow

Molecular Flow

Molecular Flow

Molecular Flow

Kinetic Theory

As analyzed by Albert Einstein in 1905, this experimental evidence for kinetic theory is generally seen as having confirmed the existence of atoms and molecules.

What is a digital Image?

http://people.cs.clemson.edu/~dhouse/courses/405/notes/pixmaps-rgb.pdf

RGB Color Spacehttp://people.cs.clemson.edu/~dhouse/courses/405/notes/pixmaps-rgb.pdf

RGB Color Space

RGB Color Cube

Fuzzy Image Processinghttp://tizhoosh.uwaterloo.ca/Fuzzy_Image_Processing/theory.htm

1. Fuzzy Geometry2.Measures of Fuzziness and Image Information3. Fuzzy Inference System4. Fuzzy Mathematical Morphology5. Fuzzy Measure Theory6. Fuzzy Grammars7. Combined Appoach 8. Extension of Classical methods

Why Fuzzy image processing?

1. Fuzzy techniques are powerful tools for knowledge representation and processing.

2. Fuzzy techniques can manage the vagueness and ambiguity efficiently.

In many image processing applications, we have to use expert knowledge to overcome the difficulties (e.g. object recognition, scene analysis).

http://tizhoosh.uwaterloo.ca/Fuzzy_Image_Processing/why.htm

Fuzzy Image Processing

Kinds of Image Fuzzification

http://tizhoosh.uwaterloo.ca/Fuzzy_Image_Processing/theory.htm

Fuzzification- Process of transforming crisp values into grades of membership for linguistic terms of fuzzy sets.

Structure of Fuzzy Image Processing

Fuzzification process (coding of images)

History of Fuzzy Logic

1.2. Statement of the Problem

Existing technology

either expensive (Meng, Yuxing, Wuchang, & Juntao, 2011)

less accurate (Doorhy, 2011)

Proposed solution

A natural gas leak detection system that is cheap and accurate by using Fuzzy Neuro Approach.

Reasons behind:

Fuzzy neuro approach was used by many researchers in detection of water leaks and the like.

1.3. Objective of the Study

The main purpose of this study is to develop a water quality and natural

underwater gas leak detection system using fuzzy-neuro image processing. This study specifically aims:

1.To develop an aquarium prototype for an underwater water quality and gas leak detection experimental set-up,

2. To determine the quality of water using fuzzy logic algorithm,

3. To develop an image processing algorithm to detect water bubbles on both clean and average environment,

4. To develop a neural network algorithm to detect gas leaks in the underwater pipeline system using bubble formation,

5. To verify the accuracy, reliability and robustness of the proposed algorithm in determining gas leaks underwater.

1.4. Significance of the Study

The creation of study will trigger awareness in the stakeholders in the area and create a worldwide impact. These stakeholders include the companies, people occupying in the area, government, investors, and experts. .

1.5. Scope of the StudyScope: Natural Gas Lab scale PC based model Contaminant addition

2.Review of Related Literature

2.1. Properties of Natural Gas

2.Review of Related Literature

2.2. Leak Detection Method known by Science(Murvay & Silea, 2012)

2.Review of Related Literature

2.3. Non-conventional Algorithm

2.3.1. Genetic Algorithm (Sivanandan, & Deppa , 2008).

2.3.2. Artificial Neural Network-94.2% (Carvalho et al., 2006)2.3.3. Fuzzy Logic-90% (Da Silva et al., 2005)

2.Review of Related Literature

2.4. Image Processing

Leak detection in water ( Ekuakille et al., 2014)

3. Framework

3.1. Conceptual Framework

Clearer View of Prototype Set-up

Fuzzy Logic Structure

Determination of Underwater Quality

Detection of Leakage

1. Bubble Formation2. Pressure Decrease Test3. Pressure Increase Test4. Pressure Difference Test

http://www.leakdetection-technology.com/science/leak-detection-and-measuring-methods

Detection of Bubbles1. Classification2. Feature Extraction 3. Pattern Recognition

Techniques can be used in image processing:1. Pixelation2. Neural Networks3.Linear Filtering4. Principal Component Analysis5. Hidden Markov Models6. Anisotropic Diffusion7. Partial Diffential Equations8. Self-organizing Maps9. Wavelets

http://thesisconcepts.com/digital-image-processing

Pixelation

http://en.wikipedia.org/wiki/Pixelation#/media/File:Dithering_example_undithered.png

1. Object Recognition2. Motion Recognition

http://thesisconcepts.com/digital-image-processing

Object RecognitionAppearance-based method1. Edge Matching2. Divide and Conquer Search3. Greyscale Matching4. Gradient Matching5. Histogram of receptive field responses6. Large model bases

http://en.wikipedia.org/wiki/Outline_of_object_recognition

Feature-based method1. Interpretation trees2. Hypothesize and test3. Pose consistency4. Pose Clustering5. Invariance6. Geometric hashing7. Scale-invariant feature Transform (SIFT)8. Speed Up Robust Features (SURF)

http://en.wikipedia.org/wiki/Outline_of_object_recognition

http://thesisconcepts.com/digital-image-processing

Motion Detection

Motion detection is the process of detecting a change in position of an object relative to its surroundings or the change in the surroundings relative to an object.

Motion can be detected by:

1. Infrared (Passive and active sensors)2. Optics (video and camera systems)3. Radio Frequency Energy (radar, microwave and tomographic motion detection)4. Sound (microphones and acoustic sensors)5. Vibration (triboelectric, seismic, and inertia-switch sensors)6. Magnetism (magnetic sensors and magnetometers)

http://en.wikipedia.org/wiki/Motion_detection

Optical Flow

Optical flow or optic flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene

Neural Network Structure

4. Methodology

5. Summary Answering Specific Objectives1. To develop an aquarium prototype for an underwater water quality and gas leak detection experimental set-up,

2. To determine the quality of water using fuzzy logic algorithm,

3. To develop an image processing algorithm to detect water bubbles on both clean and average environment,

4. To develop a neural network algorithm to detect gas leaks in the underwater pipeline system using bubble formation,

5. To verify the accuracy, reliability and robustness of the proposed algorithm in determining gas leaks underwater.

1. To develop an aquarium prototype for an underwater water quality and gas leak detection experimental set-up

2. To determine the quality of water using fuzzy logic algorithm

Quality of Water Expected recognition rate Actual recognition rate

Clean 80% More than 80%

Dirty 80% More than 80%

3. To develop an image processing algorithm to detect water bubbles on both clean and average environment

Quality of Water Previous recognition rate Actual recognition rate

Clean 80% More than 80%

Dirty 80% More than 80%

4. To develop a neural network algorithm to detect gas leaks in the underwater pipeline system using bubble formation

Quality of Water Previous recognition rate Actual recognition rate

Clean 90% More than 90%

Dirty 90% More than 90%

5. To verify the accuracy, reliability and robustness of the proposed algorithm in determining gas leaks underwater.

Proposed Algorithm Actual recognition rate

Fuzzy Logic Water Detector More than 80%

Neural Network gas leak detector

More than 90%

Image Detector Created More than 90%

5. Appendix A: Gantt Chart

5. Appendix B: Costing

Thank You For Listening!The Researcher is now ready to

answer questions.