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Fault diagnosis of high-speed train (HST) bogie plays an indispensable role in guaranteeing the safety and stability of the daily operation of HST. Up to now, the well-established fault diagnosis models of HST bogie, which are usually trained by supervised learning mechanism, require that the training and testing data satisfy the same probability distribution. Consequently, those methods could lose their efficacy when the probability distribution of the testing data is changed owing to the variation of HST running speed and the labeled data corresponding to the changed speed is absent. To address this, a stepwise adaptive convolutional network (SACN) is proposed to learn the domain-invariant features of vibration data in different speed domains. Moreover, a novel in-and-out stepwise transfer method is designed to deal with the scenario of continuous change in vehicle speed. The experimental tests of the proposed method are conducted using the dataset acquired by SIMPACK via the HST model CRH380 A, mainly addressing the single and compound failure classification problem of the three key components of HST bogie, namely, the air spring, antiyaw damper, and lateral damper. Overall, the proposed SACN achieves an average accuracy of 96.1%, demonstrating the remarkable performance of domain adaptation in performing fault diagnosis of HST bogie under variant running speeds. [ABSTRACT FROM AUTHOR]

As one of the most important systems of high-speed train (HST), bogie system matters when it comes to the safety and reliability of HST operation. In order to strengthen feature propagation and alleviate vanishing gradients problems in training deep-learning networks, a novel Dense-Squeeze Network based on one-dimensional convolutional neural networks (1D-CNN) is proposed for bogie fault diagnosis. On the one hand, dense blocks are capable of facilitating feature reuse and increasing the depth of feature propagation. On the other hand, the squeeze operation automatically measures the importance level of each feature channel, and then enhance the useful features and suppress the useless features for the current task. The experimental results tested by the HST model CRH380 A at different speeds verify the effectiveness of the proposed method, and the accuracy of fault diagnosis converges to 99.66%. Compared with other deep-learning-based methods, such as 1D-CNN, Long Short-Term Memory (LSTM) and DenseNet, the superiority of the proposed scheme is demonstrated clearly. [ABSTRACT FROM AUTHOR]

Background: The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately. Purpose: The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer. Methods: We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC). Results: The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p =0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p =0.005) for predicting ALNM in breast cancer. Conclusion: The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis. [ABSTRACT FROM AUTHOR]

Realizing the accurate fault diagnosis of high-speed train (HST) bogie is of great significance for ensuring the safe operation of HSTs. This article proposes a novel fault diagnosis method to identify the fault states of HST bogie and localize the positions of faulty components simultaneously by virtue of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and 1-D convolutional neural network (1-D CNN). First, the raw vibration signal is decomposed into multiple intrinsic mode functions (IMFs) via the ICEEMDAN. Then, the high-frequency components of IMFs obtained by ICEEMDAN are selected as the fault features of the HST bogie. Afterward, the 1-D CNN model is adopted to learn the deeper features from the high-frequency components to conduct fault classification and localization. The prediction accuracy of the proposed method is 99.3% and 98.7% on two bogie data sets, respectively. Meanwhile, experimental results demonstrate the effectiveness of the proposed method in identifying both the categories and the locations of faulty components, whose superiority has been verified by the comprehensive comparison analysis with traditional deep learning methods and state-of-art methods, including residual-squeeze net, convolutional recurrent neural network, and DenseNet. [ABSTRACT FROM AUTHOR]

Intelligent fault diagnosis of bogie is fundamental for the reliability, stability, and security of a high-speed train (HST). However, thorny issues, including complicated structure of bogie and complex motion states of HST, aggravate the difficulty of research, where traditional approaches for fault diagnosis might fail. In this article, CapsNet, a contemporary novel neural network architecture, in which a single unit called capsule known for its uniqueness of multidimension and abundant spatial information, is adopted to accomplish the recognition and classification of seven working conditions of a HST bogie, comprising both single and compound faults. The experimental accuracy achieved is 96.65%, which proves the efficiency and potency of CapsNet in this regard. The ability of CapsNet to diagnose faults of a bogie in this article exceeds that of a convolutional neural network (CNN), especially for compound ones. Moreover, the method employed is capable of extracting features from raw data automatically and is independent of expert experience or knowledge about signal processing. [ABSTRACT FROM AUTHOR]

The vibration and friction triggered by the long-term operation of stacker inevitably lead to the damage problem of stacker’s track. In order to ensure the stable operation of the stacker, a novel scheme is proposed to address an accurate damage localization problem of the stacker’s track in industrial environment based on ensemble empirical mode decomposition-empirical mode decomposition (EEMD-EMD) and density-based spatial clustering of applications with noise (DBSCAN). First, the original electric current signal with unbalanced distribution is processed by fixed-interval smoothing and cubic Hermite interpolation algorithms. Second, the EEMD-EMD method, which solves the mode-mixing problem, is adopted to decompose the preprocessed signal into well-defined intrinsic mode functions (IMFs). Third, based on Hilbert–Huang transform (HHT) of the IMFs, the preprocessed signal and the difference of average instantaneous amplitudes in two adjacent time points are considered as the 2-D feature vector input of the DBSCAN algorithm. As such, damage locations of the stacker’s track are detected by means of the outliers that are obtained by the DBSCAN algorithm. The efficiency of the proposed localization scheme and its superiority over the existing cooperative damage localization methods, namely, the box-plot method, the pulse coupling neural network (PCNN) and wavelet transform (WT) theory method, and the box-plot and WT method, are verified through experiments using the data provided by State Grid Measuring Center of China. [ABSTRACT FROM AUTHOR]

Fault diagnosis of high-speed train (HST) bogie is essential in guaranteeing the normal daily operation of an HST. In prior works, feature extraction from multisensor vibration signals mainly relies on signal processing methods, which is independent of the classification process. Based on convolutional neural networks (CNNs), this paper presents a novel fault diagnosis system using the residual-squeeze net (RSNet), which is directly applicable to raw data (time sequences) and does not require any signal transformation or postprocessing. In this network, information fusion is achieved by using the convolutional layer. More specifically, via the squeeze operation, an optimal combination of channels is learnt by training the network. Experimental results obtained by using SIMPACK simulation data demonstrate the effectiveness of the proposed approach in both complete failure case and single failure case, with diagnosis accuracy near 100%. The proposed approach also shows good performance in identifying the locations of faulty components. Comparisons between RSNet and competitive methods shows the advantages of RSNet for fault classification. [ABSTRACT FROM AUTHOR]

Performance monitoring and fault diagnosis for the critical component of bogie is very important. Simulation data of high speed train bogie fault signal was selected in the data experiment. Based on multiresolution analysis, wavelet entropies were extracted to reflect the complexity level of the vibration signal on scales. In the high dimension composed by several wavelet entropy features, the dates from four fault patterns were classified and recognition rate is above 90% when the speed over 200 km/h. The wavelet entropy feature is effective for fault signal analysis of high speed train bogie. [ABSTRACT FROM AUTHOR]