Abstract
To quickly and effectively identify an axle box bearing fault of high-speed electric multiple units (EMUs), an evolutionary online sequential extreme learning machine (OS-ELM) fault diagnosis method for imbalanced data was proposed. In this scheme, the resampling scale is first determined according to the resampling empirical formulation, the K-means synthetic minority oversampling technique (SMOTE) method is then used for oversampling the minority class samples, a method based on Euclidean distance is applied for undersampling the majority class samples, and the complex data features are extracted from the reconstructed dataset. Second, the reconstructed dataset is input into the diagnosis model. Finally, the artificial bee colony (ABC) algorithm is used to globally optimize the combination of input weights, hidden layer bias, and the number of hidden layer nodes for an OS-ELM, and the diagnosis model is allowed to evolve. The proposed method was tested on the axle box bearing monitoring data of high-speed EMUs, on which the position of the axle box bearings was symmetrical. Numerical testing proved that the method has the characteristics of faster detection and higher classification performance regarding the minority class data compared to other standard and classical algorithms.
Highlights
In recent years, remarkable achievements have been made in the construction of high-speed electric multiple units (EMUs), with over 3500 sets of standard high-speed EMUs put into service and a railway operating distance reaching 29,000 km in China alone
Comparing the four algorithms on the two datasets, we found that the MS-artificial bee colony (ABC)-OSELM performed the best in terms of both the G-mean and the F1-measure
F1-measure the four classification datasets, we found that the MS-ABC-OSELM performed the best in terms of both the G-mean and the F1-measure
Summary
Remarkable achievements have been made in the construction of high-speed electric multiple units (EMUs), with over 3500 sets of standard high-speed EMUs put into service and a railway operating distance reaching 29,000 km in China alone. The axle box bearing, which is one of the essential parts of a high-speed. It poses a risk to railway safety when a train fully loaded with passengers is running at a high speed [2]. It is of great importance to research techniques for bearing fault diagnosis [3,4,5,6]. As imbalanced as the axle box bearing states typically are, being able to identify the minority class faults with accuracy is much more crucial than identifying the normal ones in the majority class [7]. To shed light on this, when a faulted axle box bearing is diagnosed as normal, it might lead to derailment and death, causing great casualties. Regarding a normal state axle box as a faulted one would probably bring a temporary halt to the train.
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