Penentuan Set-Point DCS Pompa Sentrifugal Menggunakan SPC dan Regresi Linear
DOI:
https://doi.org/10.37525/sp/2026-2/2497Keywords:
Distributed Control System, Machine Learning, Pemeliharaan Prediktif, Pompa Sentrifugal, Statistical Process ControlAbstract
Keandalan operasional pompa sentrifugal sangat bergantung pada ketepatan sistem peringatan dini Distributed Control System (DCS). Permasalahan utama yang dikaji adalah penentuan batas peringatan (set-point) DCS yang umumnya bersifat statis, sehingga kurang responsif terhadap dinamika mekanis mesin secara real-time. Penelitian ini bertujuan untuk mengoptimalkan set-point alarm dan merancang model pemeliharaan prediktif. Metode yang digunakan adalah Statistical Process Control (SPC) serta algoritma Machine Learning, dengan mengintegrasikan data historis mekanis (vibrasi dan suhu bearing) beserta parameter operasional proses (debit dan tekanan). Hasil perhitungan SPC menetapkan batas aman operasi otomatis (trip) secara dinamis pada nilai vibrasi 3,57 mm/s dan suhu 92,11°C. Sintesis dari evaluasi menunjukkan bahwa nilai tersebut terbukti lebih protektif dan aman dibandingkan dengan ambang batas bahaya pada standar umum evaluasi getaran mekanis internasional. Penjelasan hasil analisis korelasi mengonfirmasi adanya hubungan linier yang positif antara kenaikan suhu terhadap vibrasi, sementara fluktuasi debit terbukti tidak memicu stres mekanis yang signifikan. Selanjutnya, metode klasterisasi berhasil memetakan profil kondisi pompa menjadi tiga tingkat risiko operasional secara otomatis, dan model regresi linear mampu memproyeksikan tren vibrasi berbasis kenaikan suhu bearing. Kesimpulan yang diperoleh adalah implementasi analitik terpadu ini mampu menghasilkan rekomendasi set-point yang sangat presisi dan sistem peringatan dini adaptif guna mencegah kerusakan aset serta meminimalkan risiko terhentinya operasi produksi secara mendadak.
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