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In his research, Adryan sought to bridge the gap between the principles of failure physics and modern technology by developing an accurate and efficient Deep Learning\u2013based method for predicting Remaining Useful Life (RUL). He integrated the principles of Predictive Maintenance (PdM) with a hybrid CNN-GRU architecture capable of learning hidden patterns in engine sensor data while simultaneously forecasting degradation history. The CNN component extracts inter-sensor correlations to understand spatial relationships, whereas the GRU models temporal degradation, enabling the engine\u2019s performance history to be captured with precision.<\/p>\n\n\n\n
\u201cThis research is not merely about calculating numbers or predicting machine performance. Its primary goal is to ensure passenger safety and airline operational efficiency by predicting failures before they occur,\u201d said Adryan. This approach is referred to as Spatio-Temporal because it is capable of capturing the physical relationships of the engine across both space and time simultaneously.<\/p>\n\n\n\n
This research utilized the C-MAPSS dataset from NASA, which provides run-to-failure data and models interactions among engine components based on principles of energy and mass. The dataset enables the model to learn real degradation patterns during engine operation, unlike other datasets that focus solely on control systems or flight dynamics.<\/p>\n\n\n\n
\u201cWith this CNN-GRU technology, we can detect engine degradation patterns that are typically hidden from manual inspections, enabling maintenance to be carried out in a timely and more efficient manner,\u201d Adryan added.<\/p>\n\n\n\n
The research findings show that the CNN-GRU architecture is capable of handling high noise levels in real operational data\u2014an issue that conventional methods often fail to address\u2014and can adapt to six different operating conditions. The use of a two-gate GRU mechanism makes model training 11.98% faster than the industry-standard LSTM, without compromising accuracy. This enables real-time RUL prediction with minimal computational load.<\/p>\n\n\n\n <\/p>\n\n\n\n The research supervisor, Dr. Budhy Kurniawan, emphasized that this innovation provides a tangible contribution to the aviation industry, as it can help airline operators minimize the risk of engine failure and optimize maintenance scheduling. Co-supervisor Drs. Sastra Kusuma Wijaya, Ph.D., added that the findings also support industry sustainability by reducing material waste caused by excessive maintenance. Adryan aims for this technology to be implemented in real-time aircraft fleet management systems, enabling operators to perform maintenance proactively before failures occur.<\/p>\n\n\n\n The open defense was also attended by the Head of the Indonesian Halal Product Assurance Agency (BPJPH), Dr. Haikal Hassan, ST., MT., as well as a member of the Advisory Board of the Sharia Economic Society (MES) and former President Director of Bank Muamalat, DR (HC) Ahmad Riawan Amin. This achievement reflects the commitment of the Faculty of Mathematics and Natural Sciences (FMIPA) at the Universitas Indonesia to advancing research that is relevant to industry and societal challenges, while further strengthening the faculty\u2019s reputation as a center of scientific excellence and technological innovation at both the national and international levels.<\/p>\n\n\n\n <\/p>","protected":false},"excerpt":{"rendered":" Keselamatan penerbangan menjadi perhatian utama di era transportasi udara modern. Pada Kamis (12\/2), Adryan Fitra Azyus resmi meraih gelar doktor di FMIPA Universitas Indonesia (UI) dengan predikat sangat memuaskan setelah mempresentasikan disertasi berjudul \u201cPrediksi Sisa Masa Pakai Mesin Pesawat Jet Menggunakan Metode CNN-GRU (Convolutional Neural Network \u2013 Gated Recurrent Unit)\u201d. Sidang terbuka digelar di Aula […]<\/p>\n","protected":false},"author":12,"featured_media":14388,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-14387","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-berita"],"blocksy_meta":[],"yoast_head":"\n![\"\"](\"https:\/\/sci-ui.id\/wp-content\/uploads\/2026\/02\/sci-442.jpg\")
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