scholarly journals Forecasting the Number of Offset Printing Machine Breakdowns Using the Support Vector Machine (SVM) Metdhod

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Nafis Khumaidah ◽  
Tedjo Sukmono
Keyword(s):  
Support Vector Machine ◽  
Setup Time ◽  
Support Vector ◽  
Offset Printing ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Machine Breakdowns ◽  
One Year ◽  
A Company ◽  
Printing Machine

PT. MJT is a company engaged in manufacturing that produces various types of plastic tubes for cosmetic packaging. Production activities at PT. MJT uses an intermittent process, which in the printing division requires a longer total setup time because this process produces various types of specifications of goods to order. This has an effect on the amount of engine breakdown. The purpose of this research is to try the method of forecasting the number of breakdowns for offset printing machines at PT. MJT. One of the methods used in this research is the Support Vector Machine method. Support Vector Machine is a method that can help predict the number of breakdowns that will be experienced by the offset printing machine at PT. MJT. Support vector machine is a method that can reduce the error value in forecasting compared to other methods. From this research, it is hoped that it can produce a forecast of the number of breakdowns for offset printing machines at PT. MJT for a period of one year or twelve periods.

scholarly journals A Bayesian least-squares support vector machine method for predicting the remaining useful life of a microwave component

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668596 ◽  
Author(s):  
Fuqiang Sun ◽  
Xiaoyang Li ◽  
Haitao Liao ◽  
Xiankun Zhang
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Remaining Useful Life ◽  
Lifetime Prediction ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Microwave Component ◽  
Useful Life ◽  
Bayesian Least Squares

Rapid and accurate lifetime prediction of critical components in a system is important to maintaining the system’s reliable operation. To this end, many lifetime prediction methods have been developed to handle various failure-related data collected in different situations. Among these methods, machine learning and Bayesian updating are the most popular ones. In this article, a Bayesian least-squares support vector machine method that combines least-squares support vector machine with Bayesian inference is developed for predicting the remaining useful life of a microwave component. A degradation model describing the change in the component’s power gain over time is developed, and the point and interval remaining useful life estimates are obtained considering a predefined failure threshold. In our case study, the radial basis function neural network approach is also implemented for comparison purposes. The results indicate that the Bayesian least-squares support vector machine method is more precise and stable in predicting the remaining useful life of this type of components.

Urban water resources allocation and shortage risk mapping with support vector machine method

2015 ◽  
Vol 81 (2) ◽  
pp. 1209-1228 ◽  
Author(s):  
Qian Zhang ◽  
Xiujuan Liang ◽  
Zhang Fang ◽  
Tao Jiang ◽  
Yubo Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Risk Mapping ◽  
Support Vector ◽  
Resources Allocation ◽  
Urban Water ◽  
Water Resources Allocation ◽  
Machine Method ◽  
Support Vector Machine Method
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