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Penerapan Metode K-Nearest Neighbors (kNN) pada Bearing

Authors

  • Anggi Priliani Yulianto Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Islam Bandung
  • Sutawanir Darwis Universitas Islam Bandung

DOI:

https://doi.org/10.29313/jrs.v1i1.16

Keywords:

Sisa Masa Manfaat, Bearing, Tantangan Prognostik

Abstract

Abstract. Monitoring the condition of the engine is a top priority to avoid damage. To know the condition of the bearing, it is important to know the remaining useful life of the machine. In the IEEE PHM 2012 Prognostic Challenge platform provides real data related to accelerated bearing degradation carried out under constant operating conditions and online controlled variables of temperature and vibration (with horizontal and vertical accelerometers). In this platform, the data used is bearing2_3 data in the horizontal direction which has a duration of about 2 hours, calculated RMS every 1/10 second (2560 data). In this study machine learning based modeling will be done using the k-nearest neighbor (kNN) method to determine the prediction of RMS bearings. The kNN method is based on the classification of objects based on training data that is the closest distance to the object. kNN is a nonparametric machine learning algorithm which is a model that does not assume distribution. The advantage is that the class decision line produced by the model can be very flexible and very nonlinear. The smallest MSE value was obtained at k = 16 with MSE value = 0.157579. After getting the optimum k value, proceed with predicting a RMS of 97 lags and identifying bearing performance in several phases.

Abstrak. Pemantauan kondisi mesin menjadi prioritas utama untuk menghindari adanya kerusakan. Untuk mengetahui kondisi bantalan, penting untuk mengetahui sisa masa manfaat dari mesin tersebut. Dalam platfrom IEEE PHM 2012 Prognostic Challenge ini menyediakan data nyata terkait dengan degradasi bantalan yang dipercepat yang dilakukan di bawah kondisi operasi konstan dan variabel yang dikendalikan secara online berupa suhu dan getaran (dengan akselerometer horizontal dan vertikal). Dalam platform ini, data yang digunakan adalah data bearing2_3 pada arah horizontal yang berdurasi sekitar 2 jam ini dihitung RMS setiap 1/10 detik (2560 data). Dalam penelitian ini akan dilakukan pemodelan berbasis machine learning menggunakan metode k-nearest neighbor (kNN) untuk mengetahui prediksi RMS bearing. Metode kNN didasarkan pada klasifikasi terhadap objek berdasarkan data pelatihan yang jaraknya paling dekat dengan objek tersebut. kNN merupakan salah satu algoritma pembelajaran mesin yang bersifat nonparametrik yakni model yang tidak mengasumsikan distribusi. Kelebihannya adalah garis keputusan kelas yang dihasilkan model tersebut bisa jadi sangat fleksibel dan sangat nonlinier. Nilai MSE terkecil diperoleh pada k = 16 dengan nilai MSE = 0,157579. Setelah mendapatkan nilai k optimum, dilanjutkan dengan memprediksi RMS sebanyak 97-lag serta mengidentifikasi performance kinerja bearing dalam beberapa fase.

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Published

2021-07-06