Abstract
Bogies are crucial for the safe operation of rail transit systems and usually work under uncertain and variable operating conditions. However, the diagnosis of bogie faults under variable conditions has barely been discussed until now. Thus, it is valuable to develop effective methods to deal with variable conditions. Besides, considering that the normal data for training are much more than the faulty data in practice, there is another problem in that only a small amount of data is available that includes faults. Concerning these issues, this paper proposes two new algorithms: (1) A novel feature parameter named spectral kurtosis entropy (SKE) is proposed based on the protrugram. The SKE not only avoids the manual post-processing of the protrugram but also has strong robustness to the operating conditions and parameter configurations, which have been validated by a simulation experiment in this paper. In this paper, the SKE, in conjunction with variational mode decomposition (VMD), is employed for feature extraction under variable conditions. (2) A new learning algorithm named weighted self-adaptive evolutionary extreme learning machine (WSaE-ELM) is proposed. WSaE-ELM gives each sample an extra sample weight to rebalance the training data and optimizes these weights along with the parameters of hidden neurons by means of the self-adaptive differential evolution algorithm. Finally, the hybrid method based on VMD, SKE, and WSaE-ELM is verified by using the vibration signals gathered from real bogies with speed variations. It is demonstrated that the proposed method of bogie fault diagnosis outperforms the conventional methods by up to 4.42% and 6.22%, respectively, in percentages of accuracy under variable conditions.
Highlights
Urban rail transit systems are critical for large modern cities
By using actual data that had been acquired from A-type trains by a Chinese metro company, the WSaE-Extreme learning machine (ELM) has been verified as being usable for bogie fault diagnosis with imbalanced data under variable conditions
The simulation results show that the spectral kurtosis entropy (SKE), in conjunction with variational mode decomposition (VMD), has strong robustness under operating conditions and parameter crations and can be used for feature extraction under variable conditions
Summary
Urban rail transit systems are critical for large modern cities. With the development of urban rail traffic, more and more metro trains are being placed into service, and the safe and reliable operation of these trains has become a hot topic. In this paper, inspired by the Shannon entropy, a novel feature extraction method named spectral kurtosis entropy (SKE) is proposed to extract fault characteristics under variable conditions. Since in practice the normal samples acquired from bogies are much more than the faulty ones, bogie fault diagnosis algorithms suffer from the imbalance of data To deal with this issue, this paper proposes a novel algorithm named weighted self-adaptive evolutionary extreme learning machine (WSaE-ELM). This method gives each sample an optimizable sample weight to rebalance the training data and employs self-adaptive differential evolution algorithm to optimize these weights and the parameters of the hidden neurons.
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