Multi-Time-Series Variable Indirect Truck Lifting Detection Based on Motor Data Visualization
In automated container terminal operations,simultaneous lifting of containers and trucks has led to significant safety issues,including damage to goods and vehicles and,in severe cases,human fatalities.To mitigate these issues,a novel approach,Motor-data Engineering and image processing-based Indirect truck lifting detection Network(MEIN),is introduced.MEIN collects three-phase current and voltage data from cranes and employs feature engineering based on physical formulas to calculate numerous related time-series physical quantities.It combines a Sliding window with Tomek-Synthetic Minority Oversampling Technique(SMOTE)for sampling,which enhances sample size and balances category quantities.This process converts multi-time-series variables into an image format for classification via EfficientNet.In tests,MEIN demonstrated robust detection capabilities,even in challenging conditions such as adverse weather or obscured tires,achieving an Area Under the receiver operator characteristic Curve(AUC)above 0.997 across various test regions.Compared to traditional detection methodologies such as lidar and computer vision,MEIN offers notable advantages including reduced cost,heightened accuracy,reduced computational demands,and enhanced resilience to environmental disturbances.Its successful implementation in Wuhan,Qingdao,Qinzhou,and Meishan underscores its its effectiveness in improving safety at automated container terminals.
国家科技期刊平台
NSTL
万方数据
