Time-to-failure (TTF) prognostics plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert-Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches.

Effective fault diagnosis of rotating machinery plays a pretty important role in the enhanced reliability and improved safety of industrial informatics applications. Although traditional intelligent fault diagnosis techniques such as support vector machine, extreme learning machine and convolutional neural network might achieve satisfactory accuracy, a very high price is caused by marking all samples manually. In this paper, a novel fault diagnosis method of the rotating machinery is proposed by integrating semi-supervised generative adversarial nets (SSGANs) with wavelet transform (WT), namely WT-SSGANs. The proposed WT-SSGANs method involves two parts. In the first part, WT is adopted to transform 1-D raw vibration signals into 2-D time-frequency images. In the second part, the 2-D time-frequency images are inputted into the built SSGANs model to realize fault diagnosis with little labeled samples. The advantage of the built model is that the unlabeled samples might be made full use of through an adversarial learning mechanism. Finally, two case studies are implemented to verify the proposed method. The results indicate that it can achieve higher accuracy and use less labeled samples than other existing methods in literatures. In addition, its performance in stability is pretty good as well. Competitive and promising results are still achieved when working conditions are changed.

Accurate and timely bearing fault diagnosis is crucial to decrease the probability of unexpected failures of rotating machinery and improve the efficiency of its scheduled maintenance. Since convolutional neural networks (CNN) have poor feature extraction capability for sensor data with 1D format, CNN combined with signal processing algorithm is often adopted for fault diagnosis. This increases manual conversion work and expertise dependence while reducing the feasibility and robustness of the corresponding fault diagnosis method. In this paper, a novel sensor data-driven fault diagnosis method is proposed by fusing S-transform (ST) algorithm and CNN, namely ST-CNN. First of all, a ST layer is designed based on S-transform algorithm. In the ST layer, sensor data is automatically converted into 2D time-frequency matrix without manual conversion work. Then, a new ST-CNN model is constructed, and the time-frequency coefficient matrixes are inputted into the constructed ST-CNN model. After the training process of the ST-CNN model is completed, the classification layer such as softmax performs the fault diagnosis. Finally, the diagnosis performance of the proposed method is evaluated by using two public available datasets of bearings. The experimental results show that the proposed method performs the higher and more robust diagnosis performance than other existing methods.

Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to severe data distribution difference under variable working conditions, they have unsatisfactory performance of the BFD. Although the existing transfer learning (TL) methods might improve the diagnostic performance in different data distributions, fault data from these different domains in training have to be obtained. When a given bearing operates in a new working condition and fault data are not available, the TL methods might be invalid, and the BFD would be postponed. To solve the above problem, a novel zero-shot learning method named deep attention relation network (DARN) is proposed for the BFD under multiple unknown domains. The built DARN only trained by the data from a known domain might be used to diagnose fault types from unknown, but related domains without prior data input. In this method, a feature extraction module is constructed to generate representations of input samples, and a relation module is designed to calculate the relation score between the sample pairs to determine their categories. Meanwhile, a parallel attention mechanism is introduced into the DARN so as to enhance the representative ability of the built model. The results of experimental study indicate that the proposed method can make use of fault knowledge learnt from the single known domain for the BFD in the several unknown domains. The proposed DARN significantly outperforms the existing popular TL methods in diagnostic performance.

Robot-assisted bilateral upper-limb training grows abundantly for stroke rehabilitation in recent years and an increasing number of devices and robots have been developed. This paper aims to provide a systematic overview and evaluation of existing bilateral upper-limb rehabilitation devices and robots based on their mechanisms and clinical-outcomes. Most of the articles studied here were searched from nine online databases and the China National Knowledge Infrastructure (CNKI) from year 1993 to 2015. Devices and robots were categorized as end-effectors, exoskeletons and industrial robots. Totally ten end-effectors, one exoskeleton and one industrial robot were evaluated in terms of their mechanical characteristics, degrees of freedom (DOF), supported control modes, clinical applicability and outcomes. Preliminary clinical results of these studies showed that all participants could gain certain improvements in terms of range of motion, strength or physical function after training. Only four studies supported that bilateral training was better than unilateral training. However, most of clinical results cannot definitely verify the effectiveness of mechanisms and clinical protocols used in robotic therapies. To explore the actual value of these robots and devices, further research on ingenious mechanisms, dose-matched clinical protocols and universal evaluation criteria should be conducted in the future.