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

Abstract

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.

Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions

“…We fused these manual features at the feature level for improving the representation of the fault information. Then, we fed it into several classic machine learning classifiers [40][41][42][43][44][45] that are widely used in fault diagnosis tasks for comparison. The end-to-end model that we proposed in a previous work [34] used time and frequency signals as raw input signals to detect the gear fault patterns.…”

A Survey of Predictive Maintenance: Systems, Purposes and Approaches

“…To extract useful knowledge and make appropriate decisions from big data, machine learning (ML) techniques have been regarded as a powerful solution. Before going "deep", a variety of "shallow" ML algorithms are developed for the context of PdM, e.g., Artificial Neural Network (ANN) [124][125][126][127][128][129][130][131], decision tree (DT) [132][133][134][135][136], Support Vector Machine (SVM) [137][138][139][140][141], k-Nearest Neighbors (k-NN) [142][143][144][145][146][147], particle filter [148,149], principle component analysis [150,151], adaptive resonance theory [152,153], self-organizing maps [154,155], etc. In this section, a subset of well-developed ML algorithms are reviewed and briefly summarized, with a complete list of references.…”

“…DT-based ML techniques have been frequently utilized for PdM. First, due to the nature of DT, many efforts are devoted to identifying or classifying the state of the real-world system [132][133][134][135][136]. For example, Benkercha et al [134] propose a new approach based on DT algorithm to detect and diagnose the faults in grid connected photovoltaic system (GCPVS).…”

An Extended FMEA Model Based on Cumulative Prospect Theory and Type-2 Intuitionistic Fuzzy VIKOR for the Railway Train Risk Prioritization

“…Bogie system is one of the most major complex mechatronic part of railway train and can be easily prone to fail. Bogie system can account for a substantial 21.1% based on accumulation of failure data in a couple years [ 47 ]. In this paper, a specific railway train bogie system is applied to cope with the proposed FMEA model.…”