Implementation of Autonomous Maintenance and its Effect on MTBF, MTTR, and Reliability of a Critical Machine in a Beer Processing Plant
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Implementation of Autonomous Maintenance and its Effect on MTBF, MTTR, and Reliability of a Critical Machine in a Beer Processing Plant |
| 2. | Creator | Author's name, affiliation, country | Jacob Sawai Ben; Papua New Guinea University of Technology; Papua New Guinea |
| 3. | Subject | Discipline(s) | |
| 3. | Subject | Keyword(s) | |
| 4. | Description | Abstract | This research is part of plant reliability improvement program of a beverage manufacturing company that specializes in beer production and packaging. Two months of machine breakdown data gathered showed that the glass bottle filler/crowner had the longest downtime of 96.62 hours and was fast becoming a critical machine. With the plant fully automated, availability of the critical machine became a key issue because breakdowns were more likely to affect production and product quality. To reduce maintenance pressure, autonomous maintenance (AM) was introduced on the bottle filler/crowner as part of total productive maintenance. The aim of the AM program was to optimize machine availability through education and upskilling of shop floor operators to a level where they can take care of minor maintenance jobs on their equipment so that skilled maintenance people can concentrate on value-added tasks and technical repairs. Before implementing AM, the bottle filler/crowner had an average MTBF of 87.42 hours, average MTTR of 1.15 hours, a shift (12 hours) reliability of 87 %, and a complete day (24 hours) reliability of 76 %. After implementing AM for two months, there was a noticeable increase in MTBF to 113.27 hours, a decrease in MTTR to 0.87 hours, and an increase in machine reliability to 90 % and 81 % respectively for 12 and 24 hours operation. The results show that empowering operators in performing autonomous maintenance on their machines is key to detecting equipment failure, reduce breakdown, increase reliability, and improve machine performances. |
| 5. | Publisher | Organizing agency, location | Scholar AI LLC. |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2022-03-06 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijpsat.org/index.php/ijpsat/article/view/4068 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://dx.doi.org/10.52155/ijpsat.v31.1.4068 |
| 11. | Source | Title; vol., no. (year) | International Journal of Progressive Sciences and Technologies; Vol 31, No 1 (2022) |
| 12. | Language | English=en | en |
| 13. | Relation | Supp. Files | |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2022 Jacob Sawai Ben This work is licensed under a Creative Commons Attribution 4.0 International License. |