Palembiya, Revi (2021) Prediksi Diagnosis dan Prognosis Pemeliharaan Manufaktur pada Salah Satu Pabrik Semen di Indonesia. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dengan masuknya era Industri 4.0, berkembang berbagai sistem
Smart Manufacturing yang mampu meningkatkan tingkat produktivitas dan efisiensi operasional manufaktur secara
signifikan. Efisiensi operasional manufaktur termasuk kedalam pemeliharaan mesin manufaktur secara berkala dengan
mengetahui status kerusakan dan status umur mesin bekerja yang dikenal dengan Predictive Maintenance (PdM). Tugas Akhir ini mengembangkan metode yang bertujuan untuk memprediksi status kerusakan mesin (status diagnosis) dan status umur mesin bekerja (status prognosis) dengan memanfaatkan data sensor yang terinstal pada mesin manufaktur salah satu pabrik semen di Indonesia menggunakan algoritma Long Short-Term Memory (LSTM). Data yang digunakan pada Tugas Akhir ini diambil dari data lima sensor Mesin Rawmill pada tahun 2015. Hasil Tugas Akhir ini berupa
aplikasi yang mampu mengklasifikasikan status diagnosis dan prognosis dengan akurasi 99%.
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With the entry of the Industry 4.0 era, various Smart Manufacturing systems have developed that are able to
significantly increase the level of productivity and efficiency of manufacturing operations. Manufacturing operational efficiency includes periodic maintenance of manufacturing machines by knowing the damage status and age status of the working machines, known as Predictive Maintenance (PdM). This final project develops a method that aims to predict engine failure status(diagnosis status) and working engine age status (prognosis status)by utilizing sensor data installed on a manufacturing machine at a cement factory in Indonesia using the Long Short-Term Memory
algorithm (LSTM). The data used in this final project is taken from the data of five machine sensors Rawmill in 2015. The result of this final project is an application that is able to classify the diagnosis and prognosis status with an accuracy 99%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Smart Manufacturing, Prediksi Status Diagnosis, Prediksi Status Prognosis, LSTM |
| Subjects: | Q Science > QA Mathematics > QA276 Mathematical statistics. Time-series analysis. Failure time data analysis. Survival analysis (Biometry) Q Science > QA Mathematics > QA278.55 Cluster analysis Q Science > QA Mathematics > QA336 Artificial Intelligence Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Revi Asprila Palembiya |
| Date Deposited: | 31 Aug 2021 07:10 |
| Last Modified: | 31 Aug 2021 07:10 |
| URI: | http://repository.its.ac.id/id/eprint/90667 |
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