This conference serves as a platform for sharing cutting-edge research and practical applications of statistical methods in engineering data analysis. It fosters collaboration and knowledge exchange in the pursuit of enhancing the quality and reliability of engineering systems.
Kernel Density Estimation (KDE) for Reliability Assessment
Explore the application of kernel density estimation techniques to assess the reliability of engineering systems by modeling failure and repair times, enabling more informed decision-making in maintenance and operations.
Nonparametric Regression for Quality Control
Investigate the use of nonparametric regression models, such as loess and spline methods, in quality control processes to detect and address variations in manufacturing and production systems, ensuring product consistency.
Survival Analysis for Engineering Systems
Delve into survival analysis methods, such as Kaplan-Meier estimation and Cox proportional hazards models, to analyze time-to-event data in engineering contexts, such as equipment lifetimes and component failures.
Nonparametric Hypothesis Testing in Experimental Design
Examine the application of nonparametric tests like the Wilcoxon rank-sum test and the Kruskal-Wallis test to assess the significance of treatment effects and factors in engineering experiments, facilitating robust conclusions.
Functional Data Analysis for Sensor Data
Explore the use of functional data analysis techniques to analyze and model high-dimensional sensor data generated by complex engineering systems, enabling real-time monitoring and anomaly detection.