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Abstract Topic: Statistics

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Bearing Degradation Prediction Based on Support Vector Regression
(a)Sutawanir Darwis, (b)Nusar Hajarisman, (c)Suliadi, (d)Achmad Widodo

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Corresponding Author
Sutawanir Darwis

Institutions
(a),b),(c)Department of Statistics, Bandung Islamic University
(d)Department of Mechanical Engineering, University of Diponegoro Semarang
(a)std.darwis[at]gmail.com, (b)nusarhajarisman[at]yahoo.com, (c)suliadi[at]gmail.com, (d)awidodo2010[at]gmail.com

Abstract
A life table gives probabilities based in failure per thousands in a given year and used to help determine premiums. The construction of life table is based on time to failure of the unit which is converted to a duration vector and used to calculate the estimate of life table using moment method. Bearings are one most present in turbine power generation. To assess turbine degradation, risk accident prognosis of bearings is needed. Predicting bearing degradation before reaching the state of risk of accident is one important issues in power generation insurance. This paper proposes a method based on support vector regression to achieve the goal. The results will be used to predict the remaining useful life of the bearings. The method is applied on PRONOSTIA dataset which is an experimental platform dedicated to test methods related to bearing health assessment. The results shows that the method can effectively model the evolution of the bearing degradation.

Keywords
bearing degradation, remaining useful life, support vector regression, power generation insurance, PRONOSTIA

Topic
Statistics

Link: https://ifory.id/abstract/Cv6g3aXpZ7fJ


Pure Premium Calculation of Rice Farm Insurance Scheme in Indonesia Based on The 4-Parameter Beta Mixture Distribution
Aceng Komarudin Mutaqin, Yayat Karyana, Siti Sunendiari

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Corresponding Author
Aceng Komarudin Mutaqin

Institutions
Department of Statistics, Bandung Islamic University

Abstract
The Indonesian Government introduced rice farm insurance scheme (Asuransi Usaha Tani Padi – AUTP program) to protect rice farm from loss caused by flood, drought and pest and disease infestations. It has been discussed the method to calculate pure premium of the rice farm insurance scheme in Indonesia with assume that the distribution of rice yield data is normal distribution, gamma distribution, or normal mixture distribution. The normal distribution can be used for the case of distribution of rice yield data in the form of symmetry. The gamma distribution can be used for the case of distribution of rice yield data in the form of skewed to the right or positively skewed. In addition, the normal and gamma distributions are categorized as unimodal distributions. It has also been discussed the method of calculating pure premium of the rice farm insurance scheme in Indonesia with the assumption that the rice yield data is normal mixture distribution. The normal mixture distribution can be categorized as multimodal distribution. Characteristic of the normal mixture distribution is suitable for rice yield data in Indonesia that contain rice yield data from several provinces. On the other hand it is also known that the 4-parameter beta mixture distribution can be applied to rice yield data in Indonesia. This distribution is more flexible than the normal mixture distribution in terms of the tail shape of the distribution. In this paper, pure premium calculation method of the rice farm insurance scheme in Indonesia is formulated through a parametric statistical approach with assume that the distribution of rice yield data is 4-parameter beta mixture distribution. The proposed method is applied to the rice yield data in The Yogyakarta Special Region province. The result show that the pure premiums is IDR 214.183/hectare/planting season.

Keywords
rice farm insurance scheme; pure premium; multimodal distribution; normal mixture distribution; 4-parameter beta mixture distribution

Topic
Statistics

Link: https://ifory.id/abstract/tUmpPYxN8zk7


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