Surface electromyography (sEMG) is extensively employed to characterize human physiological signals within Internet of Things (IoT) systems, serving as a critical component in Human-Computer Interaction (HCI) and various other applications. Although neural networks have been widely applied to intelligent recognition of sEMG signals, existing methods often face significant challenges in accuracy degradation and computationally intensive processing when handling multisubject signals. To address these issues, this paper proposes a robust surface electromyography (sEMG) intelligent recognition method based on OmniScale XceptionTime-Enhanced Deep Belief Network (OmniXceptionDBN). The method first processes raw signals using Singular Spectrum Analysis (SSA) and Fast Fourier Transform (FFT), then integrates XceptionTime, OmniScaleCNN, and Deep Belief Networks (DBN) to construct the OmniXceptionDBN algorithm for sEMG recognition. The designed integrated network for sEMG signals (i.e., the OmniXceptionDBN algorithm) achieves recognition accuracies of 97.2% for single-subject and 85.9% for multi-subject recognition scenarios without requiring dataset-specific optimizations. Our approach effectively resolves the accuracy degradation when processing across individuals and the high computational complexity inherent in traditional methods, providing an efficient solution for intelligent sEMG recognition.

Deep neural networks have promoted the technology development of fault classification and remaining useful life (RUL) prediction for mechanical equipment due to their powerful nonlinear feature extraction capability. However, the performance of traditional deep learning models is limited by the depth of networks, which is directly related to the training consumption. In addition, the parameters of networks can only be updated by retraining when faced with newly acquired data. To address the above problems, an incremental learning method based on a temporal cascade broad learning system (TCBLS) is proposed for the RUL prediction of machinery with newly acquired data. Specifically, linear and nonlinear feature information is first learned by the TCBLS. The ridge regression method is developed to calculate the weights of the network and establish an end-to-end mapping between the feature information layer and the prediction layer. Finally, the incremental learning of new data and the incremental learning of nodes are proposed for adaptively updating the weights of the network in the face of newly acquired data and insufficient prediction accuracy. The effectiveness of the proposed method is verified by four run-to-failure datasets. The comparison results with classical deep learning models show that the proposed method is promising for RUL prediction as it achieves high prediction accuracy while saving training time consumption across orders of magnitude and effectively handling newly acquired data without retraining.

The rotor-bearing system is widely used in various high-end electro-hydraulic equipment, which provides specific support, rotation, and other integral functions. However, the fluctuating working conditions of the rotor-bearing system will cause more significant disordered fluctuations in the measured signals. This article proposes a new algorithm called multiscale deep graph convolutional networks (MS-DGCNs) to alleviate this problem. The designed MS-DGCNs algorithm combines a new multiscale intra-class fine coarse-grained processing and multiscale graph convolution kernels. Accordingly, an intelligent fault diagnosis method based on MS-DGCNs for the rotor-bearing system under fluctuating conditions is designed to learn more feature representations and accuracy. First, a sliding window is employed to divide the collected vibration signals into a series of subsignals. The multiscale signal processing is performed to obtain different degrees of the fine-coarse time series. Then, a graph convolution with the multiscale convolution kernel is designed. Finally, the soft-max classifier is combined for intelligent fault diagnosis. The experimental results of the double-span rotor-bearing system under fluctuating conditions well demonstrate that the method has the higher accuracy and generalization.

The industrial gearboxes usually work in harsh and variable conditions, which results in partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of gearbox under variable conditions maybe increase the intraclass difference and reduce the interclass difference for the monitored samples. To this end, a new intelligent fault diagnosis method of gearbox based on adaptive intraclass and interclass convolutional neural network (AIICNN) under variable working conditions is proposed. The core of the proposed algorithm is to apply the designed intraclass and interclass constraints to improve the distribution differences of samples. Meanwhile, the adaptive activation function is added into the 1-D convolutional neural network (1dCNN) to enlarge the heterogeneous distance and narrow the homogeneous distance of samples. Specifically, the training sample subset with intraclass and interclass spacing fluctuations under variable conditions is first converted into frequency domain through the fast Fourier transform (FFT), and the designed AIICNN algorithm is employed for model training. Afterward, the testing subset is provided to the trained AIICNN algorithm for fault diagnosis. The experimental data of the planetary gearbox test rig verify the feasibility of the proposed diagnosis method and algorithm. Compared with other methods, this method can eliminate the difference of sample distribution under variable conditions and improve its diagnostic generalization.

Advanced mechatronics equipment requires reliable and effective performance degradation assessment to guarantee long-term operations. Current data-driven predictions endow the operation and maintenance of mechatronic equipment flexibly and intelligently. However, the sufficient and labeled data in real industrial scenes may not be satisfied, resulting in negative impacts of overfitting and time-consuming annotations. In this article, we propose a novel prognostic model, namely unsupervised meta gated recurrent unit (UMGRU) containing a dual-cycle learning architecture with the designed clustering assignment module to deal with few-shot prognostics under unlabeled historical data. It integrates the strength of double gradient based optimizations for abstracting general degradation knowledge and offering a sensitive model status for precisely online adaptation with limited on-site data. Besides, mini-batch pseudolabels are automatically assigned within each inner cycle learning and further participate in parameter upgrades. Finally, both experimental and industrial data are used to verify the effectiveness of UMGRU.

Health evaluation is attracting more and more attention in the domain of machinery prognostic and health management (PHM). Meanwhile, few studies have been devoted to health evaluation under variable working conditions and few shots learning, which are common situations under industrial sites. Thus, this shortcoming becomes the motivation of our study. We propose subspace meta-learning (SML) that integrates the strengths of knowledge transfer, constructing the statistically relevant latent subspace, and meta learning, realizing few shots prognostics. To be specifically, time-frequency images are first extracted with sliding windows along with the vibration signals across different life experiments of rolling bearings. Then, two-dimensional domain adaptation based on high order statistical properties is utilized to construct latent subspace and generate meta degradation knowledge. Finally, the convolutional layer based meta learning under model-agnostic learning mode is set up based on the time-frequency degradation knowledge. For a transparent test of our proposed SML health evaluation methodologies, public FEMTO-ST bearing datasets are employed for verifications, and comparisons are also conducted between existing prediction methods. Prediction performances reveal that the superiority of SML under few-shot prognostics.