Poisoning. Heat Exchanger. Fouling. Disturbance parameter changes ... LNG System .... SCOPE AND LIMITATION. â¢For simulation, Aspen HYSYS will be used.
AN ADADELTA-ENHANCED ARTIFICIAL NEURAL NETWORK BASED FAULT DETECTION AND DIAGNOSIS IN A SERIES OF DISTILLATION COLUMNS Art Philippe Bucaneg Jerome Pablo
WHAT IS A FAULT? A fault is any “unpermitted deviation of at least one characteristic property of the system.” Fault
Process parameter changes
Catalyst Poisoning Heat Exchanger Fouling
Disturbance parameter changes Extreme changes in parameters
Actuator problems
Sticking Valve
Sensor Problems
Bias
Complete Failure
Drifting
Chiang, Russel and Braatz (2001)
Precision Degradation
Production of Acrylic Acid from Propylene (Suo, et.al, 2015)
No Fault Detection Yes
Fault Identification
Fault Diagnosis
Fault Recovery Chiang, Russel & Braatz, 2001
No Fault Detection Yes
Fault Identification
Fault Diagnosis
Fault Recovery Chiang, Russel & Braatz, 2001
No Fault Detection Yes
Fault Identification
Fault Diagnosis
Fault Recovery Chiang, Russel & Braatz, 2001
No Fault Detection Yes
Fault Identification
Fault Diagnosis
Fault Recovery Chiang, Russel & Braatz, 2001
LEVELS OF FAULTS • SINGLE FAULT, SINGLE EQUIPMENT • A single root fault that originates from one equipment
• SIMULTANEOUS, MULTIPLE FAULTS, SINGLE EQUIPMENT • More than one root fault occurring at the same time from one equipment
• SIMULTANEOUS MULTIPLE FAULTS, MULTIPLE EQUIPMENT • More than one root fault occurring at the same time from more than one equipment
LNG System
SOLUTION TO FAULT DETECTION AND DIAGNOSIS: BIG DATA
Logistic Regression SVM
Binary
Density based Clustering
ANN
Density
Decision Tree
gdbscan
Ordered Logit model
Classification
Some knowledge of cluster structure
Ordinal
ANN
K-means Number of Clusters
Clustering
Unknown Structure
Decision Tree
Self-organizing Map
Multiclass
Hierarchical clustering
Nominal Supervised Learning
Unsupervised Learning
Multinomial Logit Model Hierarchical Logit Model Artificial Neural Network Decision Tree
Principal Component Analysis
Dimensionality Reduction
Support Vector Machine K-nearest Neighbors
Multi-Dimensional Scaling Continuous
Self-organizing Map
Multiple Regression ANN
Regression
ANN Discrete
Decision Tree
Simplified guide to statistical and machine learning choices (D. Beck et. al, 2016)
BIG DATA INFORMATICS
Logistic Regression SVM Binary
Density based Clustering
ANN
Density
Decision Tree
gdbscan
Ordered Logit model
Classification
Some knowledge of cluster structure
Ordinal
ANN
K-means Number of Clusters
Clustering
Unknown Structure Unsupervised Learning
Hierarchical clustering
Decision Tree
Self-organizing Map
Multiclass
Nominal
Supervised Learning
Decision Tree
Principal Component Analysis
Dimensionality Reduction
Multinomial Logit Model Hierarchical Logit Model Artificial Neural Network
Support Vector Machine K-nearest Neighbors
Multi-Dimensional Scaling Continuous
Self-organizing Map
Multiple Regression ANN
Regression
ANN Discrete
Decision Tree
Simplified guide to statistical and machine learning choices (D. Beck et. al, 2016)
COMPARISON OF VARIOUS DIAGNOSTIC METHODS Observer
Digraphs
Abstraction Hierarchy
Expert Systems
QTA
PCA
Neural Networks
Quick detection And diagnosis
?
?
Isolability
Robustness
Novelty Identifiability
?
?
Classification Error
Adaptability
?
Explanation Facility
Modelling Requirement
?
Storage and Computation
?
?
Multiple Fault Identifiability
COMPARISON OF VARIOUS DIAGNOSTIC METHODS Observer
Digraphs
Abstraction Hierarchy
Expert Systems
QTA
PCA
Neural Networks
Quick detection And diagnosis
?
?
Isolability
Robustness
Novelty Identifiability
?
?
Classification Error
Adaptability
?
Explanation Facility
Modelling Requirement
?
Storage and Computation
?
?
Multiple Fault Identifiability
ARTIFICIAL NEURAL NETWORK Example of ANN accuracy
AN ADADELTA-ENHANCED ARTIFICIAL NEURAL NETWORK BASED FAULT DETECTION AND DIAGNOSIS IN A SERIES OF DISTILLATION COLUMNS Art Philippe Bucaneg Jerome Pablo
SIGNIFICANCE OF THE STUDY Artificial neural networks… • Does not require the use of design equations • Represents the system into a more compact model
We chose the multiple distillation columns for our study because… • It is the premier separation method in the chemical and petroleum industries, the subject of distillation control has been extensively studied for many decades. (Luyben, 2013) • Most of these studies have looked at individualcolumns in isolation. (Luyben, 2013)
OBJECTIVES OF THE STUDY • To improve the existing mode of FDD through the use of ANN • To utilize an algorithm for the neural network for optimal detection time • To simulate the series distillation column system data • To provide an insight to the potential of a neural network specifically trained for single fault in the detection of multiple faults
SCOPE AND LIMITATION • For simulation, Aspen HYSYS will be used. All single faults and only some multiple will be simulated. • The ANN is meant to quicken fault detection and diagnosis in the said setup and not as a predictive model on when faults might occur. • Fault recovery is not part of the study.
METHODOLOGY 1. Learn ANN algorithm from various sources • Video tutorials • Journals • Online lectures/Slides
2. Study various optimization techniques for convergence • Gradient Descent Method • Batch • Stochastic • Mini-Batch
• Optimization Techniques • • • • •
Momentum Nesterov Momentum RMSprop ADAprop ADADelta
3. Test code with given training sets • MNIST (handwritten number recognition) • Wine Classification • Iris (Orchids) classification • Tic-Tac-Toe Simulation
METHODOLOGY 4. Simulation of faults using Aspen HYSYS • Values • Test nominal value • 2.5-15% deviation, 2.5% interval, for single error • 5% for multiple errors
• For multiple columns in series, single and multiple errors
5. Train ANN with data from simulation • Using 5%, 10%, and 15% • Only single error
6. Test ANN for single error • Using 2.5%, 7.5%, and 12.5%
7. Test ANN for multiple errors
METHODOLOGY • Test for accuracy • Error value during iteration
• % 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
# 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 # 𝑜𝑓 𝑑𝑎𝑡𝑎 𝑝𝑜𝑖𝑛𝑡𝑠
• Further optimization through ANN architecture • Number of hidden layers • Number of nodes per hidden layer • Minimize Over/underfitting
MILESTONES 1. 2. 3. 4. 5. 6. 7.
Learn ANN Generate general ANN MatLab Code Implement several convergence optimization algorithms Test on several data sets Start simulation for multiple distillation column Test on simulated faults Optimize the code
GANTT CHART Activity Learn ANN Code Writing
Learn and Apply Optimization Techniques Test Code
Lit. Rev.
2016 July
August
September
October
November
December
GANTT CHART Activity Simulation of Data Points
Testing of Data Points Further Optimization
Lit. Review Paper Writing
2017 January
February
March
April
May
IRIS PLANT CLASSIFICATION Error Value vs Epoch Graph and Accuracy per Epoch Graph
TIC TAC TOE Error Value vs Epoch Graph and Accuracy per Epoch Graph
WINE CLASSIFICATION Error Value vs Epoch Graph and Accuracy per Epoch Graph
SINGLE COLUMN (SHARMA, 2003) Error Value vs Epoch Graph and Accuracy per Epoch Graph
MNIST RESULTS
MNIST RESULTS
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