A Method for Diagnosing the Abnormal State of the OCS Compensation Device and Mid-Point Anchor Based on Data Feature Recognition
Common methods,such as manual inspection,image recognition,and fixed-point monitoring,struggle to comprehensively and accurately evaluate the status of the Overhead Contact System(OCS)compensation device and mid-point anchor.Therefore,a state anomaly diagnosis method based on data feature recognition is proposed.Firstly,two types of typical data features are summarized during abnormal conditions of both the compensation device and the mid-point anchor.Specifically,three feature quantities are defined to describe the Overhead Contact Line(OCL)height mutation in the mid-point anchor area,three to indicate the difference in OCL height on both sides of the anchor section,and three to describe the pantograph-catenary contact force mutation in the mid-point anchor area.Secondly,a sample library is constructed based on a large amount of detected data.Outlier diagnosis methods are employed to check the reliability of the samples,and Principal Component Analysis(PCA)is applied to reduce strongly correlated feature quantities,thereby forming a training sample library.Finally,a classification model is trained using Random Forest(RF),and the classification model is evaluated using overall accuracy,false positive rate,and true negative rate.The feature quantities,classification model,and field trial verification of the proposed method are conducted.The results indicate that each feature quantity exhibits good separability,verifying the reliability and universality of the summarized data features.RF achieves a classification accuracy of over 95％in both types of sample training.The diagnosis results obtained using the proposed method are highly accurate in field verification,demonstrating strong feasibility in diagnosing abnormal conditions of both the compensation device and the mid-point anchor.
万方数据
维普
