Ensuring reliable machine operation is dependent on effective fault detection. Feature indicators construction is considered as the foundation for operational condition assessment among various detection methods. However, the nuclear circulating water pump (NCWP) experiences severe disturbances due to noise and changes in operating conditions. Additionally, the complex movement of the gears causes signal interference, which may result in the feature indicators failing. To address the above issues, this paper proposes a generalized features construction method (GFC) to realize unsupervised planetary gearbox fault detection. First, a feature relevance evaluation method is presented to filter intrinsic mode functions (IMFs) obtained through ensemble empirical mode decomposition (EEMD) for noise reduction. Second, feature indicators are constructed based on interpretable frequency information. Then, global and local features are evaluated respectively to realize fault detection and further detection of faults from the sun or planetary gears. Finally, the effectiveness of the proposed method is demonstrated through data obtained from the NCWP test bench.

Domain Adaptation can transfer crossdomain diagnosis knowledge by minimizing the divergence of labeled source and unlabeled target data. However, the model neglects to maximize physics prior knowledge during feature extraction and distribution alignment, resulting in a non-interpretable model even negative transfer. Hence, a physics-informed domain adaptation network, termed Adaptive Fault attention Residual Network (AFARN), is proposed. First, an adaptive fault attention mechanism is designed to refine features guided by bearing fault characteristics, suited to generating diagnosis-relevant features. Then, several metrics are applied to minimize the marginal and conditional distribution discrepancy of features, thus generalizing the model from source to target domain. The AFARN utilizes the fault characteristics and label information simultaneously to train the model, which can enhance the distribution alignment of diagnosis-relevant features, thus providing an interpretable knowledge transfer. Finally, experiments on public and circulating water pump datasets show that AFARN can enhance fault feature learning and diagnosis accuracy.

Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (K_c) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

Journal bearings are the key components of the nuclear circulating water pump (NCWP), and accurate remaining useful life (RUL) prediction is of great significance for improving the reliability, safety, and maintenance planning of NCWP. However, it is difficult to quantify the uncertainty of bearing RUL based on the current deep learning (DL) model, resulting in a lack of credibility and effective convincing for RUL predicted by the model. Meanwhile, all existing hybrid models are basically simple combinations, and they cannot solve the uncertainty quantification problem of RUL predicted by DL. Hence, the bearing RUL prediction method based on a dynamic interactive hybrid model is proposed. First, a degradation model based on a nonlinear enhanced generalized Wiener process (EGWP) is proposed, which combines gated neural networks and time-varying drift coefficients to describe the nonlinear degradation process of bearing. Then, a corrective gated recurrent unit (CGRU) network is designed to learn and predict real-time degradation increments, and the parameters of the degradation model are dynamically updated through the history and prediction of degradation increments. Finally, the bearing RUL prediction is given by the CGRU network, and the probability density function of RUL is given by the proposed hybrid model. The performance of the proposed method is evaluated using the PHM 2012 bearing dataset and the NCWP journal bearing dataset. The results show that our proposed method can effectively predict bearing RUL and its uncertainty.