| dc.description.abstract | Cooling fans are widely used in the cooling systems of different equipment, and the
stability of the cooling fans will directly affect the performance of the equipment. Many
international standards organizations have set standards to evaluate the performance of fans,
and many fan manufacturers have proposed different methods and improvement methods to
improve the stability and using life of cooling fans, such as improving the fan assembly
structure, or using electronic circuits to monitor the performance of fans.
This paper investigates uses supervised learning techniques, including artificial neural
network (ANN), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression
(LR) to construct a cooling fan life prediction model. Since the component of the cooling fan
includes many mechanical and electronic parts, these components are affect the life of the fan.
Therefore, collects 26 key parameters in this study to establish a fan life prediction model to
improve the accuracy of life prediction of cooling fans.
According to the results of experimental, it is studies whether FAIL will occur after the
cooling fan running for 4360hr, 6360hr and 7360hr. The results can be more than 78% accuracy
at each prediction model. In addition, among the above four learning technologies, the research
results show that the best evaluation results are SVM and RF. These two algorithm technologies
can help to establish the better prediction model for fan life prediction. | en_US |