Paper Title
The Failure Mode Prediction For Themal Insulator Equipment Using Ga And K-Means

Abstract
Thermal insulation equipment for delivering frozen foods can easily cause food products to become unsanitary if it is unable to maintain a stable temperature. Therefore the ability to warn of thermal insulation equipment failure could prevent frozen foods from spoiling and could help maintain stable food product quality. This study attempts to provide an early failure-warning model for frozen food thermal insulation equipment. This pre-warning model used k-means combined with genetic algorithms to strengthen the weight of the impact factors and also attempted to attain more successful failure pre-warning capabilities. The experimental results of this study demonstrated that it is possible to provide pre-warning information for thermal insulation failure within five minutes and thereby avoid thermal insulation failure for food products. Keywords- Thermal Insulation Equipment, Failure Mode Prediction, Genetic Algorithms