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 (rosdiazli@utp.edu.my)

Cambridge University, 27 - 29 March 2018 UKSim-AMSS 20th International Conference on Modelling & Simulation   Cambridge University, 27 - 29 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

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

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

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

Maintenance cost/downtime RESEARCH MOTIVATION Unscheduled Downtime Results in Lost Revenue !! USD 100 000 Maintenance cost/downtime 15-70 % Production losses/downtime [1] PETRONAS, 2016 ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

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

ABC MOTOR RESEARCH BACKGROUND ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

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

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

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

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

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

RESULTS ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

RESULTS AND DISCUSSIONS Explained variance (%) PC# Eigen value Explained variance (%) Cumulative 1 6.5495 46.7826 2 3.4568 24.6918 71.4744 3 1.1974 8.5533 80.0277 4 0.9946 7.1048 87.1326 5 0.9113 6.5093 93.6420 6 0.4047 2.8911 96.5331 7 0.1504 1.0747 97.6079 8 0.1210 0.8645 98.4724 9 0.1104 0.7886 99.2611 10 0.0582 0.4159 99.6771 11 0.0230 0.1645 99.8416 12 0.0210 0.1507 99.9923 13 0.0008 0.0059 99.9983 14 0.0002 0.0016 100 ELECTRICAL & ELECTRONICS ENGINEERING DEPARTMENT

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

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

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

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

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