Nianbo Dong, Keith C Herman, Wendy M Reinke, Sandra Jo Wilson, Catherine P Bradshaw
Gender, Racial, and Socioeconomic Disparities on Social and Behavioral Skills for K-8 Students With and Without Interventions: An Integrative Data Analysis of Eight Cluster Randomized Trials
Prevention science : the official journal of the Society for Prevention Research
predicting anomalies manufacturing assembly lines crucial reducing time labor costs improving processes instance rocket assembly premature part failures lead significant financial losses labor inefficiencies abundance sensor data industry 40 era machine learning ml offers potential early anomaly detection however current ml methods anomaly prediction limitations f1 measure scores 50 66 prediction detection respectively due challenges like rarity anomalous events scarcity highfidelity simulation data actual data expensive complex relationships anomalies easily captured using traditional ml approaches specifically challenges relate two dimensions anomaly prediction predicting anomalies occur understanding dependencies paper introduces new method called robust interpretable 2d anomaly prediction ri2ap designed address dimensions effectively ri2ap demonstrated rocket assembly simulation showing 30point improvement f1 measure compared current ml methods highlights potential enhance automated anomaly prediction manufacturing additionally ri2ap includes novel interpretation mechanism inspired causalinfluence framework providing domain experts valuable insights sensor readings impact predictions finally ri2ap model deployed real manufacturing setting assembling rocket parts results insights deployment demonstrate promise ri2ap anomaly prediction manufacturing assembly pipelines