Integrating synthetic minority oversampling and gradient boosting decision tree for bogie fault diagnosis in rail vehicles

Bogies are critical components of a rail vehicle, which are important for the safe operation of rail transit. In this study, the authors analyzed the real vibration data of the bogies of a railway vehicle obtained from a Chinese subway company under four different operating conditions. The authors selected 15 feature indexes – that ranged from time-domain, energy, and entropy – as well as their correlations. The adaptive synthetic sampling approach–gradient boosting decision tree (ADASYN–GBDT) method is proposed for the bogie fault diagnosis. A comparison between ADASYN–GBDT and the three commonly used classifiers (K-nearest neighbor, support vector machine, and Gaussian naïve Bayes), combined with random forest as the feature selection, was done under different test data sizes. A confusion matrix was used to evaluate those classifiers. In K-nearest neighbor, support vector machine, and Gaussian naïve Bayes, the optimal features should be selected first, while the proposed method of this study does not need to select the optimal features. K-nearest neighbor, support vector machine, and Gaussian naïve Bayes produced inaccurate results in multi-class identification. It can be seen that the lowest false detection rates of the proposed ADASYN–GBDT model are 92.95% and 87.81% when proportion of the test dataset is 0.4 and 0.9, respectively. In addition, the ADASYN–GBDT model has the ability to correctly identify a fault, which makes it more practical and suitable for use in railway operations. The entire process (training and testing) was finished in 2.4231 s and the detection procedure took 0.0027 s on average. The results show that the proposed ADASYN–GBDT method satisfied the requirements of real-time performance and accuracy for online fault detection. It might therefore aid in the fault detection of bogies.