Fault Detection for Suspension System of Maglev Trains Based on Historical Health Data

Abstract

To overcome the influence of multiple operating conditions for fault detection, this paper proposes a method to detect fault for the suspension system of maglev trains. Firstly, the complex operating condition of the maglev train is divided into some simple conditions, and the operating samples are extracted through the time window in the same simple operating condition. Secondly, the features of the extracted samples are extracted by the Fast Walsh-Hadamard transform, and the noise is removed by the median filtering. Thirdly, after adopting principal component analysis to reduce the dimensionality of the feature, the mean of the feature is calculated and applied to calculate the Euclidean distance from the features of the new data obtained by the same processing in each time. Fourthly, the new Euclidean distance obeying the Gaussian distribution is obtained through the Box-Cox transformation. Finally, the fault threshold in each simple operating condition is established through the characteristics of Gaussian distribution. Taking a certain operating condition as an example, the method is compared with the three similar methods. The results reveal that the proposed method can detect the faults timely and has a low false alarm rate. Moreover, the proposed sub-healthy data are within an acceptable range. Based on these results, the proposed method can be applied in fault detection of the maglev train, providing a certain basis for fault diagnosis.

Keywords

• Multiple operating conditions
• principal component analysis
• Euclidean distance
• Box-Cox transformation
• fault detection