Yasin, Inman (2020) Predictive Maintenance (PdM) Rawmill Dan Kiln Di PT X. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
PT X merupakan perusahaan yang bergerak di industri pembuatan semen. Unit pabrik 5 milik PT X merupakan pabrik terbaru dan diresmikan pada tanggal 19 Febuari 2014. Meskipun tergolong pabrik baru, namun availability pabrik 5 terutama rawmill memiliki nilai 0.65 jauh dibawah standar availability international sebesar 0.9. Sedangkan area kiln memiliki availability sesuai dengan standar yaitu 0.9. Rendahnya availability disebabkan oleh banyaknya unplanned downtime peralatan yang terjadi terutama area rawmill. Untuk mengatasi unplanned downtime dilakukan pemeliharaan corrective action, namun hal ini belum efektif. Dengan menggunakan sistem SAP yang ada, mulai dilakukan pemeliharaan preventive dengan membuat penjadwalan di SAP sistem. Melihat banyaknya data peralatan di dalam server dan belum dioptimalkan penggunaannya, maka sistem pemeliharaan dikembangkan menjadi predictive maintenance dengan menganalisa data data peralatan yang dimiliki. Metode yang digunakan adalah Mahalanobis Taguchi. Metode ini tidak hanya mampu menghandling data data yang besar tetapi juga memiliki keunggulan untuk melakukan diagnosa kegagalan peralatan serta memprognosa time to failure peralatan.
Berdasarkan prognosa kegagalan peralatan dari metode Mahalanobis Taguchi, dilakukan peningkatan jadwal pemeliharaan yang terintegrasi dengan SAP. Hal ini dapat mencegah unplanned downtime dan menaikkan availability peralatan terutama area rawmill sebesar 0.81. Sedangkan area kiln terjadi prediksi yang tidak tepat sehingga availability turun menjadi 0.89.
==================================================================== PT X is a company engaged in the cement manufacturing industry. The factory 5 unit owned by PT X is the newest factory and was inaugurated on February 19, 2014. Although it is classified as a new factory, the availability of factory 5, especially raw mills, has a value of 0.65, far below the international availability standard of 0.9. While the kiln area has availability according to the standard, namely 0.9. The low availability was caused by a large number of unplanned downtime of equipment that occurred, especially the raw mill area. To overcome unplanned downtime, corrective action has been maintained, however, this has not been effective. By using the existing SAP system, preventive maintenance is being carried out by making a schedule in the SAP system. Seeing a large amount of equipment data on the server and its use has not been optimized, the maintenance system has been developed into predictive maintenance by analyzing the equipment data. The method used was Mahalanobis Taguchi. This method is not only capable of handling large data but also has the advantage of diagnosing equipment failure and predicting equipment time to failure.
Based on the equipment failure prognosis from the Mahalanobis Taguchi method, an integrated SAP maintenance schedule was increased. This can prevent unplanned downtime and increase the availability of equipment, especially the raw mill area by 0.81. Meanwhile, in the kiln area, there were incorrect predictions so that the availability fell to 0.89
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Availability, Predictive Maintenance, Mahalanobis Taguchi, Availability, Predictive Maintenance, Mahalanobis Taguchi |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD9622 Cement. Concrete. Cement and concrete additives T Technology > TH Building construction > TH3351 Maintenance and repair T Technology > TJ Mechanical engineering and machinery > TJ174 Maintenance and repair of machinery T Technology > TJ Mechanical engineering and machinery > TJ217.6 Predictive Control |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26101-(S2) Master Thesis |
| Depositing User: | YASIN INMAN |
| Date Deposited: | 23 Aug 2020 10:19 |
| Last Modified: | 15 Nov 2023 08:28 |
| URI: | http://repository.its.ac.id/id/eprint/80673 |
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