Murtadho, Muhammad Rizqi (2026) Integrasi Fuzzy-Best Worst Method, Fuzzy-Bayesian Network, dan Surprise Index dalam Pengembangan FMEA untuk Pemeringkatan Penyebab Kegagalan Coal Crusher PLTU Tanjung Awar-Awar. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Kegagalan peralatan pada PLTU dapat menurunkan keandalan sistem dan mengganggu kontinuitas operasi pembangkitan listrik. Penelitian ini bertujuan menentukan prioritas kegagalan coal crusher PLTU Tanjung Awar-Awar melalui integrasi Failure Mode and Effects Analysis (FMEA), Fuzzy Best Worst Method (FBWM) untuk menentukan bobot parameter FMEA, Fuzzy Bayesian Network (FBN) untuk mengestimasi probabilitas kegagalan berdasarkan hubungan kausal dan ketidakpastian, serta Surprise Index (SI) untuk menentukan prioritas risiko kegagalan. Data penelitian diperoleh dari data historis work order periode 2020–2025 dan penilaian enam pakar yang berpengalaman dalam operasi dan pemeliharaan coal crusher. Hasil pembobotan FBWM menghasilkan bobot severity, occurrence, dan detection masing-masing sebesar 0,228; 0,511; dan 0,261. Nilai reliability berdasarkan data historis sebesar 0,0079, sedangkan hasil FBN sebesar 0,0191. Kedua hasil menunjukkan bahwa reliability coal crusher masih rendah. Berdasarkan metode SI, Panel Sensor Patah akibat Korosi, Alarm Lubrication akibat Gagal Pelumasan dan Sensor Rusak, serta Vibration (>12 mm) akibat Rubber Coupling Rusak dan Bearing Damage menjadi lima prioritas risiko tertinggi. Simulasi perbaikan menggunakan Bayesian Network menunjukkan bahwa penerapan rekomendasi berdasarkan metode SI meningkatkan reliability dari 0,0191 menjadi 0,0357.
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Equipment failures in coal power plants can reduce system reliability and disrupt the continuity of electricity generation. This study aims to determine the priority of coal crusher failures at Tanjung Awar-Awar Thermal Power Plant by integrating Failure Mode and Effects Analysis (FMEA), the Fuzzy Best Worst Method (FBWM) to determine the weights of FMEA parameters, the Fuzzy Bayesian Network (FBN) to estimate failure probabilities based on causal relationships and uncertainty, and the Surprise Index (SI) to prioritize failure risks. The study utilized historical work order data from 2020 to 2025 and expert assessments from six specialists experienced in coal crusher operation and maintenance. The FBWM weighting results yielded weights of 0.228, 0.511, and 0.261 for severity, occurrence, and detection, respectively. The reliability value based on historical data was 0.0079, while the FBN result was 0.0191, indicating that the coal crusher has low reliability. Based on the SI method, the five highest-priority failure risks were Panel Sensor Failure caused by Corrosion, Alarm Lubrication caused by Lubrication Failure and Sensor Failure, and Vibration (>12 mm) caused by Rubber Coupling Failure and Bearing Damage. Improvement simulation using the Bayesian Network showed that implementing the recommendations based on the SI increased the reliability from 0.0191 to 0.0357.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Failure Mode and Effects Analysis, Fuzzy-Bayesian Network. Fuzzy-Best Worst Method, Probabilitas Kegagalan, Surprise Index, Failure Mode and Effects Analysis, Fuzzy-Bayesian Network. Fuzzy-Best Worst Method, Probability of Failure, Surprise Index |
| Subjects: | T Technology > TS Manufactures > TS174 Maintainability (Engineering) . Reliability (Engineering) |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26101-(S2) Master Thesis |
| Depositing User: | Muhammad Rizqi Murtadho |
| Date Deposited: | 30 Jul 2026 02:20 |
| Last Modified: | 30 Jul 2026 02:20 |
| URI: | http://repository.its.ac.id/id/eprint/139392 |
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