(Universitas Pertahanan RI, Indonesia)(2) I Nengah Putra Aprianto
(Universitas Pertahanan RI, Indonesia)(3) Sasono Rahardjo
(Universitas Pertahanan RI, Indonesia)*corresponding author
AbstractPemeliharaan prediktif merupakan pendekatan strategis yang semakin penting dalam menjaga kesiapan dan keandalan kendaraan militer darat, khususnya yang mengandalkan teknologi daya gerak seperti motor listrik, gearbox, dan aktuator. Dengan berkembangnya teknologi digital twin yaitu representasi digital real-time dari aset fisik pemeliharaan prediktif menjadi lebih akurat dan efektif melalui monitoring kondisi komponen, simulasi, dan analisis data berbasis kecerdasan buatan dan Internet of Things (IoT). Artikel ini menyajikan tinjauan literatur sistematis mengenai pemanfaatan digital twin dalam predictive maintenance, dengan fokus aplikasi pada kendaraan militer darat yang beroperasi di lingkungan keras dan menuntut tingkat kesiapan optimal. Berdasarkan analisis 20 jurnal dari tahun 2020 hingga 2025, artikel ini menguraikan definisi, arsitektur, dan framework digital twin yang diterapkan untuk pemantauan real-time serta prediksi kerusakan komponen daya gerak. Penelusuran juga menyoroti peran machine learning dan AI dalam meningkatkan sensitivitas deteksi dini serta integrasi IoT untuk pengumpulan data kondisi secara kontinu. Implementasi digital twin terbukti meningkatkan akurasi perawatan, mengurangi downtime, serta memperpanjang umur aset kendaraan. Studi ini menegaskan bahwa teknologi digital twin sangat potensial untuk mendukung manajemen pemeliharaan armada kendaraan militer darat TNI AD. Rekomendasi diberikan untuk pengembangan sistem yang adaptif sesuai karakteristik operasi dan lingkungan Indonesia, serta kebutuhan pelatihan bagi personel teknis agar efektif dalam mengelola teknologi ini. KeywordsDigital Twin, Predictive Maintenance, Kendaraan Darat Militer, Teknologi Daya Gerak, Internet of Things (IoT)
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DOIhttps://doi.org/10.57235/qistina.v4i2.7284 |
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