Ibny, Nazarullah (2022) Prediksi Kegagalan Komponen Pada Pesawat A320-200 Dengan Menggunakan Metode Regresi. Masters thesis, Institut Teknologi Surabaya.
Text
09211950095016-Master_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 April 2024. Download (6MB) | Request a copy |
|
Text
09211950095016-Master_Thesis.pdf Restricted to Repository staff only Download (8MB) | Request a copy |
|
Text
09211950095016-Master_Thesis.pdf Restricted to Repository staff only Download (9MB) | Request a copy |
Abstract
PT GMF Aeroasia merupakan anak perusahaan dari PT Garuda Indonesia Tbk yang didirikan pada tahun 2002. PT GMF bergerak di bidang penyedia layanan perawatan pesawat udara yang berbasis di Bandara Soekarno Hatta. Pelanggan utama PT GMF Aeroasia adalah maskapai penerbangan PT Garuda Indonesia Tbk dan PT Citilink yang melayani rute penerbangan domestik dan internasional. Proses perawatan pesawat udara bertujuan menjaga performa pesawat udara dalam keadaan optimal. Pesawat udara memiliki Flight Data Recorder (FDR) yang berfungsi untuk merekam (recording) parameter-parameter penting selama penerbangan. Data-data yang direkam tersebut dapat digunakan untuk keperluan investigasi dan monitor kondisi pesawat.
Regresi Linier merupakan analisis statistika yang memodelkan hubungan beberapa variabel menurut bentuk hubungan persamaan linier eksplisit. Metode regresi merupakan salah satu teknik analisis statistika yang digunakan untuk menggambarkan hubungan antara satu variabel respon dengan satu atau lebih variabel penjelas (prediktor). Penelitian ini dilakukan di Engineering Service PT GMF. Fokus pada penelitian ini adalah mencari model prediksi kegagalan komponen dengan memanfaatkan data FDR dengan menggunakan metode Regresi Linear
Penelitian ini menunjukkan bahwa prediksi kegagalan komponen flap pada pesawat A320-200 dapat dilakukan dengan metode regresi menggunakan variabel prediktor Altitude, Vertical Acceleration, Hydraulic Pressure dan Groundspeed dengan nilai R square sebesar 66.10%.
================================================================================================
PT GMF Aeroasia is a subsidiary of PT Garuda Indonesia Tbk which was established in 2002. PT GMF is engaged in providing aircraft maintenance services based at Soekarno Hatta Airport. The main customers of PT GMF Aeroasia are PT Garuda Indonesia Tbk and PT Citilink which serve domestic and international flight routes. The aircraft maintenance process aims to maintain aircraft performance in optimal conditions. The aircraft has a Flight Data Recorder (FDR) which functions to record important parameters during flight. The recorded data can be used for investigation and monitoring aircraft conditions.
Linear Regression is a statistical analysis that models the relationship of several variables according to the form of an explicit linear equation. The regression method is a statistical analysis technique used to describe the relationship between one response variable and one or more predictor variables. This research was conducted at Engineering Service PT GMF. The focus of this research is to find a component failure prediction model by utilizing FDR data using the Linear Regression method.
This study shows that the prediction of the failure of the flap component on the A320-200 aircraft can be done using the regression method using the predictor variables Altitude, Vertical Acceleration, Hydraulic Pressure, and Groundspeed with an R square value of 66.10%
Item Type: | Thesis (Masters) |
---|---|
Uncontrolled Keywords: | Prediksi, Kegagalan komponen, Flight Data Recorder, Regresi Linier, Prediction, Component Failure, Linear Regression |
Subjects: | H Social Sciences > HE Transportation and Communications > HE9787.5 Airplanes--Ground handling |
Divisions: | Faculty of Creative Design and Digital Business (CREABIZ) > Technology Management > 61101-(S2) Master Thesis |
Depositing User: | Nazarullah Ibny |
Date Deposited: | 14 Feb 2022 04:14 |
Last Modified: | 28 Mar 2023 07:59 |
URI: | http://repository.its.ac.id/id/eprint/93857 |
Actions (login required)
View Item |