Pseudo fourth-order moment based bearing fault feature reconstruction and diagnosis

Knowledge correlation graph-guided multi-source interaction domain adaptation network for rotating machinery fault diagnosis

Leveraging generalized knowledge from multiple source domains with rich labels to the target domain without labeled data is a more realistic and challenging issue compared with single-source domain adaptation. Furthermore, the distribution discrepancies between each source domain and the expansion of data categories increase the difficulty of aligning each source domain with the target domain. To alleviate these issues, a knowledge correlation graph-guided multi-source interaction domain adaptation network (KCGMIDAN) is developed for rotating machinery fault diagnosis. Firstly, a random mini-batch is randomly selected to update comprehensive feature representations (CFR) extracted from each data category across all domains, thus promoting the knowledge interaction of acquired CFR between the current and the next epochs. Then, a knowledge correlation graph (KCG) is established on all CFR to boost knowledge propagation among various domains. To improve the compactness of characteristics within the same category and the separation of various categories, two losses are designed in this procedure to place constraints on the relationships between categories. Finally, query samples are added into KCG to construct the extended KCG, and the recognition of samples is completed by using built deep graph network based on the extended KCG. Extensive experimental results verify that KCGMIDAN can achieve better recognition performance than existing methods.

Network Construction for Bearing Fault Diagnosis Based on Double Attention Mechanism

Aiming at the difficulty of feature extraction in the case of multicomponent and strong noise in the traditional rolling bearing fault diagnosis method, this paper proposes a bearing fault diagnosis network with double attention mechanism. The original signal with noise is decomposed into a series of intrinsic mode functions (IMFs) by the Empirical Mode Decomposition method. The Pearson correlation coefficient is discussed to filter the IMFs components for signal reconstruction. The spatial features of the reconstructed signal are extracted by attention convolutional networks. Then, time series features are extracted based on the long short-term memory method. Furthermore, the importance of temporal features is measured through a temporal attention mechanism. The Softmax layer of the constructed network is used as the classifier for fault diagnosis. Comparing this method with the existing methods of experiments, the proposed method has not only better diagnosis accuracy but also stronger antiinterference ability and generalization ability, which can accurately diagnose and classify the bearing fault types. The fault diagnosis accuracy rate for each load is above 99%.

Closed-form camera pose and plane parameters estimation for moments-based visual servoing of planar objects

Image moments are global descriptors of an image and can be used to achieve control-decoupling properties in visual servoing. However, only a few methods completely decouple the control. This study introduces a novel camera pose estimation method, which is a closed-form solution, based on the image moments of planar objects. Traditional position-based visual servoing estimates the pose of a camera relative to an object, but the pose estimation method directly estimates the pose of an initial camera relative to a desired camera. Because the estimation method is based on plane parameters, a plane parameters estimation method based on the 2D rotation, 2D translation, and scale invariant moments is also proposed. A completely decoupled position-based visual servoing control scheme from the two estimation methods above was adopted. The new scheme exhibited asymptotic stability when the object plane was in the camera field of view. Simulation results demonstrated the effectiveness of the two estimation methods and the advantages of the visual servo control scheme compared with the classical method.