Six Sigma Plant Data Analytic
For the past decades, manufacturers from various industrial sector -from petrochemical to electronics- have been adopting a long-standing periodic maintenance practice namely preventive maintenance to keep up with their manufacturing operation and prolonging their plant equipment lifespan. It is a r...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project |
| Language: | English |
| Published: |
IRC
2019
|
| Online Access: | http://utpedia.utp.edu.my/20120/1/Six%20Sigma%20Plant%20Data%20Analytic%20-%20Final%20Dissertation.pdf http://utpedia.utp.edu.my/20120/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Teknologi Petronas |
| Language: | English |
| Summary: | For the past decades, manufacturers from various industrial sector -from petrochemical to electronics- have been adopting a long-standing periodic maintenance practice namely preventive maintenance to keep up with their manufacturing operation and prolonging their plant equipment lifespan. It is a reliable strategy for long term but requires high frequency of maintenance activities which lead to more production downtime and cost. Nowadays, manufacturing industry is shifting towards new changes in their manufacturing processes and technologies due to Industrial Revolution 4.0 (IR4.0) characterized by smart systems and Internet-based solutions. Many oil and gas companies such as PETRONAS GAS undergoing large- scale digital transformation in compliance with IR4.0. One of the key elements of IR4.0 is prognostic maintenance system which encompasses Data Analytic, Internet- of-Things (IoT) and Cloud Computing where manufacturers can wirelessly monitor the condition of equipment in real-time. The system can predict the future equipment failures given the historical data of that particular instrument using prognostic techniques. PETRONAS GAS has yet to adopt a more reliable IoT-based prognostic maintenance system. Currently, the company utilizes techniques such as regression technique and statistical analysis for health equipment prognosis but lacks data analytic aspect.
|
|---|