Multi-Model Switching Based Fault Detection for the Suspension System of Maglev Train

In order to satisfy the requirements of on-line monitoring of the suspension system under complex conditions, a fault detection method for maglev train suspension system based on multi-model switching is proposed. In the proposed method, the healthy samples are extracted through the moving time window, and then the features of the healthy samples are extracted by the Fast Walsh–Hadamard transform and filtered by the median filter. Then, the normalization is used to eliminate the difference of feature vectors, and then the principal component analysis method is used to reduce the dimension and de-correlation of the feature matrix contributing to a hyper-sphere space. Finally, the health threshold and the fault threshold are determined by Euclidean distance, then fault detection models are established for various operating conditions. Taking the positive line operation condition as an example, the proposed method is compared with the method based on the original feature and support vector data description based on the original feature. The results demonstrate that the proposed method is superior to the other two methods in terms of health detection rate and false positive rate. In addition, the proposed method is of the characteristics of low computational complexity, no-parameter optimization, and good robustness. It can be applied in practical engineering providing a certain research basis for data deep mining such as fault diagnosis and fault prediction.