首页|Data from Universidad Tecnica Federico Santa Maria Update Knowledge in Machine Learning (Adopting New Machine Learning Approaches on Cox's Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions)
Data from Universidad Tecnica Federico Santa Maria Update Knowledge in Machine Learning (Adopting New Machine Learning Approaches on Cox's Partial Likelihood Parameter Estimation for Predictive Maintenance Decisions)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Mdpi
Data detailed on artificial intelligence have been presented. According to news reporting from Santiago, Chile, by NewsRx journalists, research stated, “The Proportional Hazards Model (PHM) under a Condition-Based Maintenance (CBM) policy is used by asset-intensive industries to predict failure rate, reliability function, and maintenance decisions based on vital covariates data.” Funders for this research include Anid. Our news editors obtained a quote from the research from Universidad Tecnica Federico Santa Maria: “Cox's partial likelihood optimization is a method to assess the weight of time and conditions into the hazard rate; however, parameter estimation with diverse covariates problem could have multiple and feasible solutions. Therefore, the boundary assessment and the initial value strategy are critical matters to consider. This paper analyzes innovative non/semi-parametric approaches to address this problem. Specifically, we incorporate IPCRidge for defining boundaries and use Gradient Boosting and Random Forest for estimating seed values for covariates weighting. When applied to a real case study, the integration of data scaling streamlines the handling of condition data with diverse orders of magnitude and units. This enhancement simplifies the modeling process and ensures a more comprehensive and accurate underlying data analysis.”
Universidad Tecnica Federico Santa MariaSantiagoChileSouth AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Mdpi
