Wheelset bearing is the most critical component in High-Speed Train (HST) and crucial for HST safe and efficient operation. As a wide applying method for bearing health monitoring, Support Vector Machine (SVM) may easily lead to overfitting due to giving the same attention to all samples especially when training dataset contains outliers. For measuring the sample importance corresponding with normal or outlier in feature space of SVM training process, sample significance is represented by utilizing sample spatial aggregation information of feature space in which the normal samples having a good aggregation and outliers caused by noise or uncertainty tending to deviate from the sample aggregation group. Then by introducing sample significance, an improved Significance SVM (SSVM) is proposed for enhancing the robustness of SVM with noisy samples or uncertainty. SSVM overcomes the drawback of SVM and gives outlier samples less attention during model training by assigning significant coefficients to samples in the model training process. Combing SSVM, multi-domain features and feature selection, a noise robustness fault diagnosis method is successfully applied on HST bearing vibration dataset. The experiment results verify the effectiveness and the stability of the proposed method under different noise levels.

Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data; to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.

An unforeseen fault of the key bearing of production system due to different reasons has the potential to cause an interruption in the entire production line, resulting in economic and production losses. To improve the reliability of industry production, this paper presents an intelligent diagnosis method for element rolling bearing based on the integrated interaction relationship of vibration nonlinear features. The nonlinear features of vibration signals are extracted using recurrence quantification analysis (RQA) and regrouped into different subsets of non-redundant features with the same level of discrimination ability through the technology of ReliefF-affinity propagation clustering. The weighted voting variable predictive model class discrimination (WV-VPMCD) is proposed to fully utilize the interaction of RQA features to do intelligent diagnostics for bearing faults. The experimental results showed that the WV-VPMCD outperformed the conventional intelligent diagnosis methods in terms of accuracy, consistency, stability and robustness, especially in the case of small number of samples.

Lamb waves were utilized to quantify micro-crack damage in aluminum plates, and the scattering and mode conversion of Lamb waves passing through cracks were analyzed. The dynamic time warping (DTW) method was used to match and compare each Lamb wave time series that represented different damage degrees. The matching difference between the damaged plate and undamaged plate was taken as a marker to measure the damage degree of the workpiece. At the same time, due to the pathological alignment of traditional DTW methods, the shape context (SC) profile recognition method was introduced to optimize the algorithm for calculating the distance between sampling points in the DTW method and solve the pathological alignment problem. Finally, the SC-DTW method based on Lamb waves was verified by the finite element simulation model and bending test of aluminum plates. The results showed that the method was feasible for quantifying the damage degree of aluminum plates and had a great advantage in the analysis and processing of time series in low-sampling frequency and high-noise scenarios.