Using Vehicle Interior Noise Classification for Monitoring Urban Rail Transit Infrastructure

Abstract

This study developed a multi-classification model for vehicle interior noise from the subway system, collected on smartphones. The proposed model has the potential to be used to analyze the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. To this end, first, we developed a multi-source data (audio, acceleration, and angle rate) collection framework via smartphone built-in sensors. Then, considering the Shannon entropy, a 1-second window was selected to segment the time-series signals. This study extracted 45 features from the time- and frequency-domains to establish the classifier. Next, we investigated the effects of balancing the training dataset with the Synthetic Minority Oversampling Technique (SMOTE). By comparing and analyzing the classification results of importance-based and mutual information-based feature selection methods, the study employed a feature set consisting of the top 10 features by importance score. Comparisons with other classifiers indicated that the proposed XGBoost-based classifier runs fast while maintaining good accuracy. Finally, case studies were provided to extend the applications of this classifier to the analysis of abnormal vehicle interior noise events and evaluate the effects of rail grinding.

The Application of Advanced Information Technologies in Civil Infrastructure Construction and Maintenance

“…System information model 5. Smartphone sensor (Exploration of underground structure [125,126], investigation of road [90]) (Schedule monitoring [127]) (Road detection [76][77][78], track wear assessment [128], traffic condition detection [79] and vibration monitoring [129]) 6. FOS 6.…”

Intelligent Transportation Related Complex Systems and Sensors

“…Focusing on vehicle interior noise classification, the study in [ 20 ] developed a multi-classification model that has the potential to be used to analyse the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. The work first developed a multi-source data (audio, acceleration, and angle rate) collection framework via built-in sensors in smartphone.…”

Intelligent Transportation Related Complex Systems and Sensors

“…Focusing on vehicle interior noise classification, the study in [20] developed a multiclassification model that has the potential to be used to analyse the causes of abnormal noise using statistical methods and evaluate the effect of rail maintenance work. The work first developed a multi-source data (audio, acceleration, and angle rate) collection framework via built-in sensors in smartphone.…”