A novel bagged tree ensemble regression method with multiple correlation coefficients to predict the train body vibrations using rail inspection data. (1st January 2023)

• Record Type: Journal Article
• Authors: Peng, Lele; Zheng, Shubin; Zhong, Qianwen; Chai, Xiaodong; Lin, Jianhui
• Is Part Of: Mechanical systems and signal processing. Volume 182(2023)
• Publication Date: 2023-01-01
• Subjects: Train body vibrations -- Rail inspection data -- Multiple correlation coefficients -- Regression tree algorithm -- Bagged ensemble algorithm; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109543
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The proposed method can accurately predict the vibration status of the train body. Without installing any new sensors and other monitoring equipment on the train. The novel method can reduce maintenance costs and prevent potential safety risks. The daily measurement data from GJ-5 rail inspection vehicle is used as the datasets. A multiple correlation coefficients method is used for the data pre-processing. A prediction of train body vibrations base on the bagged regression tree is established. The proposed method has the best performance, and the accuracy is up to 98%. The number of valuable training data sets is reduced by 78.3%. Abstract: Prediction of the train body vibrations induced by the train running is desirable and useful to ensure comfortable service, reliable, safe, and secure operation of railway systems. By using daily measurement data from GJ-5 rail detection vehicle, this paper presents a novel prediction algorithm, which is based on bagged tree ensemble regression with multiple correlation coefficients. To obtain the valuable data sets from a large amount of inspection data, an approach of multiple correlation coefficients is used for the data pre-processing. Then the prediction model of train body vibrations is established by combining regression tree algorithm and bagged ensemble algorithm. By training the valuable data sets, the prediction results are calculated by the bagged tree ensemble regression method. Finally, the proposed method is evaluated with experimental data and the traditional method. The experimental results show that the proposed method not only has higher accuracy but also can effectively reduce the number of the data sets, the accuracy is up to 98% and the number of valuable training data sets is reduced by 78.3%. The new method proposed in the paper can accurately predict the vibration status of the train body without installing any new sensors and other monitoring equipment on the train, which can reduce maintenance costs and prevent potential safety risks.