During the long-term operation of motors, their working conditions are changing due to the industrial demands or declining health status, and traditional diagnosis methods perform poorly in that case. This article proposes a fault diagnosis method based on graph cardinality preserved attention network (GCPAT), which can work under varying working conditions, and can be generalized to the transient state. Diagnosis results are obtained by analyzing signal-converting graphs, which are composed of nodes and edges. First, the vibration signals are converted into symmetrical snowflake images by symmetrized dot pattern (SDP) method. Second, SLIC is developed to make homogeneous super-pixels in SDP images as nodes, and form graphs according to color, texture, and distance features. Finally, the GCPAT is utilized to distinguish motor status. Compared with other state-of-art methods, the results show the out-performance of GCPAT under varying working conditions both in steady and transient state.

Data-driven intelligent diagnosis methods require sufficient labeled data during training, which are usually limited in practice. A semisupervised conditional random field-based graph attention network (CRF-GAT) algorithm is proposed in this article for fault diagnosis and condition monitoring. The proposed method combines the advantages of CRF and GAT, and therefore, it achieves semisupervised fault diagnosis by modeling the label dependency and learning object representations. The scheme is optimized with the variational expectation–maximization (EM) algorithm. Specially, the clustering with adaptive neighbor (CAN) method is introduced for constructing the graph. The proposed method is applied in induction motor (IM) and permanent magnet synchronous motor (PMSM), which achieves the identification of the motor status, fault severity, and working condition. The results show that the CRF-GAT can realize an accuracy of above 97% with even below 10% of labeled samples for training, which demonstrates that it is an effective method in semisupervised fault diagnosis.

Fault prediction of electromechanical equipment can greatly reduce its maintenance cost and prevent catastrophic damage. In order to realize the accurate fault prediction of electromechanical equipment, a fault prediction method based on spatial-temporal graph information is proposed in this paper. In the proposed method, the signal data of each intermittent monitoring time period are expressed by Markov field graph information, and the spatial-temporal correlation features of graph information are extracted and studied by multivariate spatial-temporal graph neural networks. The effectiveness of Markov graph information for different state expression is demonstrated by motor fault data from motor fault experimental platform, and the bearing fault prognostics data is used to demonstrate the feasibility and accuracy of the proposed fault prediction method. The results show that based on local spatial state information and global time correlation information of monitoring signals, this method could accomplish the accurate long-term and short-term fault prediction tasks respectively.

In order to solve the tough issue that is difficult to extract the representative fault features of the hybrid vibration signals from the various industrial applications, a motor fault diagnosis method based on image visual information and bag of words model (BoW) are proposed in this paper. Based on the redundancy information of raw signals, the mapping which is between the original fault signal and the visual information is obtained by the methods of SDP (Symmetrized Dot Pattern) image and dense SIFT (Scale Invariant Feature Transform) features. The bag of visual word (BoVW) and pyramid histogram intersection kernel support vector machine (PHIK-SVM) are applying to complete the fault diagnosis and state recognition of related motors. The test results reveal that the diagnostic accuracy of four fault states could reach 99.50%, moreover, the diagnostic accuracy of different operating conditions under each fault could reach 91.83% as well. Compared with the traditional methods, the proposed method is able to obtain the detailed characteristics of various motors from the perspective of visual knowledge, furthermore, it is more robust and without complex data processing.

Steel-tube-confined concrete (STCC) targets show excellent anti-penetration performance as a result of confinement effect of steel tube on in-filled concrete. A dynamic finite cylindrical cavity-expansion (FCCE) model with elastic radial spring was developed to analyze the confinement effect and predict the depth of penetration (DOP) of STCC targets normally penetrated by rigid sharp-nosed projectiles. Firstly, steady responses of the dynamic FCCE approximation model were obtained on the basis of the assumption of incompressibility of target concrete and failure of comminuted zone with the Heok–Brown criterion. Then, based on the dynamic FCCE approximation model, a DOP model for STCC targets impacted by rigid projectiles was proposed. Lastly, the penetration tests of STCC targets normally penetrated by 12.7 mm armor piecing projectile (APP) were taken as examples to validate the applicability of the dynamic FCCE approximation model and DOP model. The results show that in comparisons with the results based on the dynamic spherical cavity-expansion (FSCE) model, the DOP model based on the dynamic FCCE model in this paper is more applicable to predict the DOP of STCC targets penetrated by rigid conical or other sharp-nosed projectiles with a proper value of empirical constant m in the Heok–Brown criterion.