of Industrial Processes
By
Arfyan Rabbani 11401063
BACHELOR’S DEGREE / MASTER’S DEGREE in
Mechanical Engineering – Mechatronics Concentration Faculty of Engineering and Information Technology
SWISS GERMAN UNIVERSITY The Prominence Tower
Jalan Jalur Sutera Barat No. 15, Alam Sutera Tangerang, Banten 15143 - Indonesia
July 2018
Revision after Thesis Defense on 16th July 2018
Arfyan Rabbani
STATEMENT BY THE AUTHOR
I hereby declare that this submission is my own work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.
Arfyan Rabbani
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Student Date
Approved by:
Prof. Dr.-Ing. Andreas Schwung
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Thesis Advisor Date
Dr. Rusman Rusyadi
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Thesis Co-Advisor
Dr. Irvan Setiadi Kartawiria, S.T.,M.Sc.
Date
_____________________________________________
Dean Date
Arfyan Rabbani
ABSTRACT
The Application of Deep Learning for Condition Monitoring and Fault diagnosis of Industrial Processes
By
Arfyan Rabbani
Prof. Dr.-Ing. Andreas Schwung, Advisor Dr. Rusman Rusyadi, Co-Advisor
SWISS GERMAN UNIVERSITY
In this paper a condition monitoring and fault diagnosis in industrial processes is made with the deep learning algorithm, with the unlabelled data provided by the Tennessee Eastman Process it is better to use unsupervised learning type of deep learning.
Different types of autoencoders (normal autoencoder (AE), denoising autoencoder (DAE), deep autoencoder (Deep AE), and variational autoencoder (VAE) is chosen as the type of deep learning algorithm. Monitoring graphs is created by reconstruction of the data into a new statistic H2 and the Squared Prediction Error (SPE) robustly. Then the control limit formed by kernel density estimation. This method demonstrated a better result with the VAE, especially with the barely detectable faults from the test data set, such as 3, 5, 9, 10, 11, 15, 19, 20 and 21. VAE shows the overall robustness in the H2 statistic and SPE reconstruction.
Keywords: deep learning, process monitoring, autoencoders, denoising autoencoder, deep autoencoder, variational autoencoder, H2, Squared Prediction Error, Data reconstruction, control limit, kernel density estimation
Arfyan Rabbani
© Copyright 2018 by Arfyan Rabbani
All rights reserved
Arfyan Rabbani DEDICATION
I dedicate this works for me and my family who has support me through all the obstacles and as the roots of my career.
Arfyan Rabbani
ACKNOWLEDGEMENTS
I wish to thank my family and friends for their support and prayers throughout the process of this thesis. Particularly Gavneet Singh Chadha, M.Sc. that has been supportive and helpful in guiding me and giving me all the useful references. And Specially, I would like to thank Prof. Dr.-Ing. Andreas Schwung and Dr. Rusman Rusyadi for the approval of thesis to complete my bachelor’s degree and to have confidence in me to finish this thesis work. And to all my friends that has support, remind, and humor me in the time of needs
I am beyond grateful to those who I mentioned above for all the support through the journey which has been a rollercoaster ride of emotions. From which I learnt a lot not only from educational perspective but also great experiences in general.
Arfyan Rabbani TABLE OF CONTENTS
STATEMENT BY THE AUTHOR ... 2
ABSTRACT ... 3
DEDICATION ... 5
ACKNOWLEDGEMENTS ... 6
LIST OF FIGURES ... 9
LIST OF TABLES ... 10
CHAPTER 1 – INTRODUCTION ... 11
Background ... 11
Research Problem ... 12
Research Objectives ... 12
Significance of Study ... 13
Research Questions ... 13
CHAPTER 2 - LITERATURE REVIEW ... 14
Machine Learning and Deep Learning ... 14
Supervised, Unsupervised and Reinforcement Learning ... 14
Deep Learning and Process Fault Diagnosis ... 15
Principle Component Analysis ... 16
Autoencoders ... 16
Denoising Autoencoder (DAE) ... 18
Deep Autoencoder ... 19
Variational Autoencoder ... 20
Drop Out ... 22
Kernel Density Estimation (KDE) ... 23
Tennessee Eastman Process Data Set ... 24
Mini-batch Gradient Descent ... 26
CHAPTER 3 –METHODS ... 29
Tensorflow Library ... 29
Method ... 30
3.2.1. Feature Scaling of the Tennessee Eastman dataset ... 30
3.2.2. Training Neural Networks ... 31
Arfyan Rabbani
3.2.3. Testing Neural Networks ... 35
CHAPTER 4 – RESULTS AND DISCUSSIONS ... 36
Testing Dataset ... 36
Result of AE ... 36
Result of DAE ... 39
Result of Deep DAE ... 41
Result of VAE ... 43
Comparation Results ... 45
CHAPTER 5 – CONCLUSIONS AND RECCOMENDATIONS ... 54
Conclusions ... 54
Recommendations ... 55
GLOSSARY ... 56
APPENDIX ... 57
References ... 99
CURRICULUM VITAE ... 101
