Prasetya, Muhamad Andhika (2022) Implementasi Metode Weibull Analysis Dalam Perancangan Predictive Maintenance Untuk Medical Mask Machine. Diploma thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Proses produksi masker PT. Azka Mulia International memiliki kendala pada kegagalan komponen mesin pembuat masker. Kendala terjadi pada beberapa komponen, seperti: emboss, roller, cutter, dan folder. Kerusakan paling banyak terjadi pada komponen emboss. Kerusakan yang terjadi membuat emboss tidak dapat menyetak merek yang ada di sudut luar masker. Untuk melakukan perbaikan, tim teknisi harus menunggu terjadinya kerusakan, dan proses produksi berhenti untuk dilakukan perbaikan. Penyebab kerusakan tidak selalu dapat diperkirakan, sehingga harus dilakukan pengecekan keseluruhan untuk mengetahui penyebab kerusakan. Hal ini berdampak pada bertambahnya waktu downtime pada mesin pembuat masker. Diperlukan sebuah solusi yang dapat memperkirakan waktu terjadinya kerusakan emboss tersebut, sehingga dapat dilakukan pencegahan agar tidak terjadi kerusakan, dan mengurangi waktu downtime. Weibull Analysis merupakan sebuah metode statistik yang umum digunakan untuk melakukan perwatan terprediksi, metode ini memiliki 3 variabel utama dalam proses perhitungan predictive maintenance. Variabel tersebut memiliki fungsi dan tujuan masing-masing yang digunakan untuk melakukan prediksi. Tiga variable tersebut adalah reliability, failure rate, dan, mean time between failure. Secara berurutan, fungsi reliability memiliki fungsi untuk mengetahui nilai keandalan komponen emboss setelah digunakan dalam suatu periode waktu, failure rate berfungsi untuk mengetahui kondisi tingkat kegagalan dari suatu komponen berdasarkan tiga kondisi utama yaitu, early life, useful life, atau wearout life. Mean time between failure memiliki fungsi untuk mengetahui rerata waktu suatu komponen dapat berjalan sebelum terjadinya kerusakan. Hasil prediksi fungsi reliability terhadap data kerusakan didapati bahwa penurunan keandalan akan melambat seiring penggunaan komponen, dan akan memasuki fase penurunan tertinggi setelah penggunaan 333 jam atau 13 hari secara terus menerus. Pada fungsi failure rate menunjukkan komponen sedang dalam fase early life dengan tingkat kerusakan meningkat setelah melewati waktu penggunaan selama 333 jam atau 13 hari secara terus menerus. Pada fungsi rerata waktu kegagalan, didapati waktu ideal penggunaan adalah selama 97 jam, atau 7 hari.
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PT. Azka Mulia International has a problem with a component failure of the mask making machine. Constraints occur in several components, such as: emboss, roller, cutter, and folder. The most damage occurs to the embossed component. The damage that occurs makes the emboss unable to print the brand on the outer corner of the mask. To carry out repairs, the technician team must wait for the damage to occur, and the production process stops for repairs to be carried out. The cause of the damage is not always predictable, so an overall check must be carried out to determine the cause of the damage. This has an impact on increasing downtime on the mask making machine. We need a solution that can estimate the time of the embossed damage, so that prevention can be done to avoid damage, and reduce downtime. Weibull Analysis is a statistical method commonly used to perform predictive maintenance, this method has 3 main variables in the predictive maintenance calculation process. These variables have their respective functions and purposes that are used to make predictions. The three variables are reliability, failure rate, and, mean time between failure. Sequentially, the reliability function has a function to determine the reliability value of the embossed component after being used for a period of time, the failure rate serves to determine the failure rate of a component based on three main conditions, namely, early life, useful life, or wearout life. The mean time between failure has a function to determine the average time a component can run before the occurrence of damage. The results of the reliability function prediction on the damage data are found that the decline in reliability will slow down with the use of components, and will enter the highest decline phase after 333 hours or 13 days of continuous use. The failure rate function shows that the component is in the early life phase with the level of damage increasing after 333 hours or 13 days of continuous use. In the average failure time function, it is found that the ideal time of use is 97 hours, or 7 days. Failure Rate; Mean Time between Failure; Machine Downtime; Reliability; Weibull Analysis. PT. Azka Mulia International has a problem with a component failure of the mask making machine. Constraints occur in several components, such as: emboss, roller, cutter, and folder. The most damage occurs to the embossed component. The damage that occurs makes the emboss unable to print the brand on the outer corner of the mask. To carry out repairs, the technician team must wait for the damage to occur, and the production process stops for repairs to be carried out. The cause of the damage is not always predictable, so an overall check must be carried out to determine the cause of the damage. This has an impact on increasing downtime on the mask making machine. We need a solution that can estimate the time of the embossed damage, so that prevention can be done to avoid damage, and reduce downtime. Weibull Analysis is a statistical method commonly used to perform predictive maintenance, this method has 3 main variables in the predictive maintenance calculation process. These variables have their respective functions and purposes that are used to make predictions. The three variables are reliability, failure rate, and, mean time between failure. Sequentially, the reliability function has a function to determine the reliability value of the embossed component after being used for a period of time, the failure rate serves to determine the failure rate of a component based on three main conditions, namely, early life, useful life, or wearout life. The mean time between failure has a function to determine the average time a component can run before the occurrence of damage. The results of the reliability function prediction on the damage data are found that the decline in reliability will slow down with the use of components, and will enter the highest decline phase after 333 hours or 13 days of continuous use. The failure rate function shows that the component is in the early life phase with the level of damage increasing after 333 hours or 13 days of continuous use. In the average failure time function, it is found that the ideal time of use is 97 hours, or 7 days.
| Item Type: | Thesis (Diploma) |
|---|---|
| Additional Information: | RSEO 620.004 6 Pra i-1 2022 |
| Uncontrolled Keywords: | Failure Rate; Mean Time between Failure; Machine Downtime; Reliability; Weibull Analysis |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3070 Automatic control |
| Divisions: | Faculty of Vocational > 36304-Automation Electronic Engineering |
| Depositing User: | Mr. Marsudiyana - |
| Date Deposited: | 16 Jul 2026 01:47 |
| Last Modified: | 16 Jul 2026 01:47 |
| URI: | http://repository.its.ac.id/id/eprint/135154 |
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