Recently, cross-domain fault diagnosis based on transfer learning methods has been extensively explored and well-addressed when class-balance data with supervision information are available. However, data under machine faulty states are mostly difficult to collect; there is a huge divide between current transfer learning methods based on implicit class-balance data and real industrial applications. In this article, we propose a class-imbalance adversarial transfer learning (CIATL) network with input being imbalanced data to learn domain-invariant and knowledge. Within this framework, class-imbalance learning is embedded into the adversarial training process to learn class-separate diagnostic knowledge with imbalanced data, double-level adversarial transfer learning including marginal and conditional distribution adaptations is conducted to learn domain-invariant knowledge. Extensive experiments on a planetary gearbox rig with imbalanced data verify the effectiveness and generalization of the proposed method and show its superior performance over contrastive transfer learning methods. Moreover, the proposed method relaxes the underlying assumption that the success of current transfer learning regimes is rooted in class-balance data and extends the application of the transfer learning method for real-industrial cross-domain diagnosis tasks.

In recent years, unsupervised domain adaptation-based methods have been widely developed for intelligent bearing fault diagnosis across various working conditions. However, a considerably more challenging and practical fault diagnosis scenario in which the source and target domains are respectively collected from bearings across different positions and machines is urgent to be addressed. To solve this issue, an innovative end-to-end domain conditioned joint adaptation network (DCJAN), which is composed of a domain conditioned feature extractor, two classifiers, and a domain discriminator is presented. On the one hand, the domain conditioned feature extraction structure is designed to relax totally-shared network assumptions in feature extraction and learn more domain-specialized features for cross-domain fault diagnosis of bearings. On the other hand, a joint adaptation strategy is implemented for diagnostic knowledge transfer across domains, in which domain-level and class-level adaptations are respectively achieved by domain-adversarial training and bi-classifier adversarial training. Extensive experiments including cross-position fault diagnosis and cross-machine fault diagnosis of bearings indicate the validity and superiority of the proposed method.

Early detection and intervention of cerebral palsy can promote neural remodeling in the process of brain development, thus reducing the negative effects of cerebral palsy. In this paper, we proposed a novel method for early prediction of infant cerebral palsy based on General Movements Assessment (GMA) theory with RGB-D videos. Firstly, we explored the human pose recognition in supine position based on RGB-D videos. Then we further apply it to auto-GMA. Specifically, we employ current pose estimation method on RGB images to achieve the infant full body 2D key points. By combining the depth information, the 3D movement of infant in supine position can be obtained. Then the infant's movement complexity index is achieved by extracting the infant's whole-body movement characteristic. In order to verify the effectiveness of the method, we did some experiments on a public dataset consisting 12 real recorded infants' movement RGB-D videos, with 4 of the samples were diagnosed as abnormal infants by a GMA expert. We use expert GMA ratings of these recorded movements as the gold standard. Our method achieved state-of-the-art with sensitivity of 100%, specificity of 87.5%, and accuracy of 91.7%. The results show that the method has great potential in assisting doctors in diagnose infant cerebral palsy.

Domain adaptation (DA)-based methods have been broadly developed for cross-domain fault diagnosis of machinery, in which the target and source label spaces are identical. However, a more challenging and practical diagnostic scenario in which the source label space subsumes target one is remaining to be solved. To address this issue, an innovative two-stage dual-weight consistency-induced partial DA (DCPDA) network, consisting of two feature extractors, a classifier, a domain discriminator, a dual-weight consistency-induced reweighting (DCR) module, and a Wasserstein distance-based-DA (WDA) module, is proposed. In the pretrained stage, a well-trained model on the source domain is acquired. In the fine-tuned stage, the DCR, which conducts a dual-weight consistency-induced weighting strategy, obtains a bi-level compound weight from both class- and sample-level weights. Embedded with the bi-level weight, the WDA is encouraged to transfer the source knowledge with mutual agreement from double-level weights across domains. Accordingly, the DCPDA is capable of alleviating negative transfer dramatically and mapping the source diagnostic knowledge into the target domain selectively. Extensive experiments indicate the validity and advantage of the proposed DCPDA.

Objective: The infant pose estimation is very important for the evaluation of infants’ brain development and motor quality. Here, we propose a new method to quickly build a large-scale depth image pose dataset and, on this basis, establish the Xi’an Jiaotong University Infant Depth Pose (XJTU-IDP) dataset, and then, we also propose a new method based on joint feature coding (JFC) to estimate the infants’ posture from depth image. Methods: First, we use Kinect V2 to collect depth and color images synchronously and use the proposed framework to build a large dataset. Then, we encode the offset vector in the confidence region of each joint of the infant and export the joint positioning and connection encoding through our convolutional neural network (CNN) model. The proposed method is tested from two datasets (including 16 and 27 clinical actual infants, respectively). Results: When applied to pose estimation, the median root mean square distance (RMSD) calculated among all limbs is 4.12 pixels, which is much lower than the baseline (9.06 pixels) on babyPose dataset and RMSD in XJTU-IDP dataset is 3.93 pixels, which achieves the best in comparison with the state-of-the-arts. Conclusion: The proposed framework of the building dataset in our work is effective, and the JFC can enrich the network information. Significance: Our work not only provides a larger infant pose dataset to make up for the deficiencies of babyPose but also proposes a new framework for quickly establishing high-quality dataset. The proposed pose estimation method significantly improves the state-of-the-art in automatic measurement of infants’ health status.

objective: Early diagnosis of infant cerebral palsy (CP) is very important for infant health. In this paper, we present a novel training-free method to quantify infant spontaneous movements for predicting CP. Methods: Unlike other classification methods, our method turns the assessment into a clustering task. First, the joints of the infant are extracted by the current pose estimation algorithm, and the skeleton sequence is segmented into multiple clips through a sliding window. Then we cluster the clips and quantify infant CP by the number of cluster classes. Results: The proposed method was tested on two datasets, and achieved state-of-the-arts (SOTAs) on both datasets using the same parameters. What’s more, our method is interpretable with visualized results. Conclusion: The proposed method can quantify abnormal brain development in infants effectively and be used in different datasets without training. Significance: Limited by small samples, we propose a training-free method for quantifying infant spontaneous movements. Unlike other binary classification methods, our work not only enables continuous quantification of infant brain development, but also provides interpretable conclusions by visualizing the results. The proposed spontaneous movement assessment method significantly advances SOTAs in automatically measuring infant health.