Analisis Konvergensi Ukuran Sampel Baseline untuk Penetapan Statistically-Derived Threshold pada Sistem Monitoring Kondisi Motor Induksi Tiga Fasa Berbiaya Rendah
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Metode statistically-derived threshold (SDT) berbasis ? ± k? banyak digunakan pada sistem monitoring kondisi motor induksi berbiaya rendah, namun jumlah sampel baseline N umumnya ditentukan secara ad-hoc tanpa kriteria konvergensi eksplisit. Makalah ini menganalisis konvergensi parameter statistik (?, ?) dan threshold (?+2?, ?+3?) terhadap N pada tiga parameter kondisi motor induksi tiga fasa—suhu (DS18B20), arus (SCT013+ADS1115), dan magnitude getaran (MPU6050)—menggunakan dataset baseline 600 sampel dari sistem monitoring real-time berbasis Arduino/Modbus TCP/SCADA. Analisis menggunakan running statistics pada N = 30–600, kriteria konvergensi ganda yang diusulkan (perubahan relatif lokal < 1% dan deviasi global terhadap nilai akhir < 2% selama lima evaluasi berturut-turut), serta selang kepercayaan bootstrap 95% (B = 2000). Hasil menunjukkan perilaku konvergensi yang sangat berbeda: threshold arus konvergen pada N ? 40 dan getaran pada N ? 150, sedangkan threshold suhu tidak konvergen sejati dalam 600 sampel akibat transien warm-up termal yang membuat data non-stasioner (pergeseran threshold ?13%). Kriteria lokal saja terbukti menghasilkan konvergensi semu pada data yang drift perlahan. Dua rekomendasi praktis dihasilkan: panjang baseline harus ditetapkan per parameter, dan baseline termal hanya boleh diakuisisi setelah kondisi tunak termal tercapai. Kerangka analisis yang diusulkan dapat direplikasi untuk sistem monitoring kondisi berbiaya rendah lainnya.
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