1. Reasons for not attending to present at UKSim 2018
No budget is allocated for author to present
Sponsors are quite late to reply since there are a lot of procedures
Reference person: AP Ir Dr Rosdiazli Ibrahim

2. Cambridge University, 27 - 29 March 2018
UKSim-AMSS 20th International Conference on Modelling & Simulation
Cambridge University, March 2018
Authors:
Nurfatihah Syalwiah Binti Rosli
Assoc. Prof. Ir. Dr. Rosdiazli Ibrahim
Assoc. Prof. Ir. Dr. Idris Ismail
Title:
Application Of Principle Component Analysis In Resolving Influential Factor Subject To Industrial Motor Failure

3. INTRODUCTION RESEARCH BACKGROUND METHODOLOGY RESULTS CONCLUSION
CONTENTS
CONTENTS
INTRODUCTION
RESEARCH BACKGROUND
INDUSTRIAL MOTOR
PRINCIPLE COMPONENT ANALYSIS
METHODOLOGY
RESULTS
CONCLUSION
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

4. ROTATING EQUIPMENT MAINTENANCE
INTRODUCTION
ROTATING EQUIPMENT MAINTENANCE
Reactive maintenance focuses on repairing an asset once failure occurs-fail n fix
Proactive maintenance, however, focuses on avoiding repairs and asset failure through preventive and predictive methods.
preventive maintenance doesn’t use algorithms to predict when maintenance is needed. Instead, it schedules tasks based on time passed or historical collected data
Prognostic: determines how likely it is for a failure to take place, and also how soon it will happen. In other words, prognosis means to apply predictive maintenance methods in order to evaluate the trends of machine condition
 predictive maintenance uses algorithms to identify trends in data and predict when failure is likely to occur. Because this method collects real-time data about equipment’s performance, the predictive maintenance usually happens while equipment is operating.
Repaired costs incurred to bring an asset back to an earlier condition or to keep the asset operating at its present condition 
prevention cost by using such product defect avoidance strategies as: statistical process control, quality engineering, staff training and various quality management tools.
This is example of the case.use historical data(failure data, condition monitoring data) to predict the failures
Figure 1: Failure Statistics for Electric
Motor Components [1]
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

5. ROTATING EQUIPMENT MAINTENANCE
INTRODUCTION
ROTATING EQUIPMENT MAINTENANCE
Prevention cost: strategies to avoid defect
Repaired cost: keep the asset operating at its present condition
Reactive maintenance focuses on repairing an asset once failure occurs-fail n fix
Proactive maintenance, however, focuses on avoiding repairs and asset failure through preventive and predictive methods.
preventive maintenance doesn’t use algorithms to predict when maintenance is needed. Instead, it schedules tasks based on time passed or historical collected data
Prognostic: determines how likely it is for a failure to take place, and also how soon it will happen. In other words, prognosis means to apply predictive maintenance methods in order to evaluate the trends of machine condition
 predictive maintenance uses algorithms to identify trends in data and predict when failure is likely to occur. Because this method collects real-time data about equipment’s performance, the predictive maintenance usually happens while equipment is operating.
Repaired costs incurred to bring an asset back to an earlier condition or to keep the asset operating at its present condition 
prevention cost by using such product defect avoidance strategies as: statistical process control, quality engineering, staff training and various quality management tools.
This is example of the case.use historical data(failure data, condition monitoring data) to predict the failures
Figure 2: Cost associated with maintenance strategies
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

6. Maintenance cost/downtime
RESEARCH MOTIVATION
Unscheduled Downtime Results in Lost Revenue !!
USD
Maintenance cost/downtime
15-70 %
Production losses/downtime
[1] PETRONAS, 2016
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

7. COMPARISON OF MAINTENANCE APPROACHES ROTATING EQUIPMENT MAINTENANCE
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

8. ABC MOTOR RESEARCH BACKGROUND
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

9. RESEARCH BACKGROUND Motor Parameter Description 1. RTD1-5 2.
Motor Voltage 3.
Red Current Phase 4.
Active Power 5.
Motor Air Temperature 6.
Cold Air Temperature
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

10. Discharge temperature
RESEARCH BACKGROUND
Process Parameter
Description 1.
Air to Plant 2.
Air booster discharge 3.
Discharge temperature 4.
Discharge pressure
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

11. PRINCIPLE COMPONENT ANALYSIS (PCA)
PROBLEM STATEMENT
PURPOSE: To reduce dimensionality while maintaining information as much as possible
METHODOLOGY
Step 1: Find mean of inputs
Step 2: Calculate deviation from the mean
Step 3: Calculate the covariance matrix
Step 4: Calculate the eigenvectors and eigenvalues
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

12. PROBLEM STATEMENT RESEARCH METHODOLOGY Data Collection Data Filtering
Data Normalization
Data Simulation using PCA
Most influential parameter identification
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

13. RESULTS Data Finding December 2015-July 2017 Interval 1 minute
Total data
875,520 (608 days)
Healthy data
807,840
Fault data
67,680
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

14. RESULTS
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

15. RESULTS AND DISCUSSIONS Explained variance (%)
PC#
Eigen value
Explained variance (%)
Cumulative 1
6.5495 2
3.4568 3
1.1974
8.5533 4
0.9946
7.1048 5
0.9113
6.5093 6
0.4047
2.8911 7
0.1504
1.0747 8
0.1210
0.8645 9
0.1104
0.7886 10
0.0582
0.4159 11
0.0230
0.1645 12
0.0210
0.1507 13
0.0008
0.0059 14
0.0002
0.0016
100
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

16. RESULTS AND DISCUSSIONS
The cumulative percentage is the successive proportions of explained variance.
To determine how many principle components should be considered is based on the drop of eigenvalue, or explained variance.
The first four principle components explain 87% of the variation.
This is an acceptably large percentage to be considered for the correlation analysis
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

17. RESULTS AND DISCUSSIONS
PC#
Group of Parameters
PC value 1
RTD1
RTD2
RTD3
RTD4
RTD5
HAT
CAT
0.37
0.38
0.35
0.36
0.33 2
ATP
ABD
CURRENT
POWER
0.46
0.50
0.48 3 TD
-0.33
0.87 4
VOLTAGE
0.80
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

18. RESULTS AND DISCUSSIONS
Based on the principle components interpretation, the numbers are large in magnitude either positive or negative direction are being chosen as the most variables correlated with each component.
PC#1, seven of the original parameters are strongly correlated. When one of these parameters increase, the other parameters also increase.
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

19. conclusion CONCLUSION
Predictive Maintenance can be improved its accuracy and function by adding analytics algorithm to predict the failure based on the most influential parameters to the equipment failure.
Factors that contribute to the industrial motor failure are successfully determined using PCA techniques.
The resolved of this influential parameters is beneficial for plant management and can used for the purpose of predictive maintenance
ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

20. THANK YOU