Electrohydrostatic Actuator (EHA) systems operating under variable conditions suffer from data distribution shifts and feature overlapping of compound faults, leading to degraded diagnostic performance. Existing methods often struggle to align features across different working domains and fail to accurately distinguish coupled fault patterns due to signal entanglement. To address these challenges, this paper proposes a Multi-Condition Feature Aligned Capsule Network (MCFACN) for cross-domain compound fault diagnosis. An Adaptive Integrated Maximum Mean Square Discrepancy (AIMMSD) metric, incorporating higher-order statistics and mean constraints, is proposed to capture distribution differences more comprehensively, Furthermore, an aggregated attention routing mechanism is employed to integrate multi-condition source-domain data with convolutional and capsule features, enhancing domain adaptation and feature separability. Extensive experiments on multi-condition transfer tasks demonstrate the superiority of the proposed method. Specifically, MCFACN achieves an average diagnostic accuracy of 99.4%, validating its effectiveness and robustness for reliable aerospace applications.

In the era of big data and intelligent sensing, deep neural networks provide new impetus for prognostics and health management (PHM) with their powerful feature extraction capabilities. However, the pursuit of performance through increased network depth and complexity concurrently escalates the number of hyperparameters and model intricacy, thereby exacerbating the inherent opaque nature and restricting their deployment in complex industrial settings. To address this dilemma, this article develops a machine health prognosis framework with ex-ante interpretability based on complex domain time-frequency network (CDTFN). Specifically, this article first investigates the intrinsic bonds between network convolution and time-frequency transforms. Building upon this foundation, four complex observation operators with trainable parameters are designed for extracting fault-related time-frequency information, embedding it into the CDTFN as a preprocessing layer. Simultaneously, by extending the forward and backward propagation mechanisms of real-valued networks to the complex domain, the proposed CDTFN gains the capability to fuse complex-valued time-frequency information and establish end-to-end mapping from feature representation layers to prediction labels. The effectiveness and accuracy of the proposed prognosis framework based on CDTFN are verified by public and self-built run-to-failure rolling bearings datasets. The detailed experimental results further demonstrate its distinct advantages in interpretability and generalization capability.

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.

Complex neural networks with deep structures are beneficial for solving problems such as fault classification and health prediction of industrial equipment due to their powerful feature extraction capabilities. Unfortunately, corresponding complex models designed based on deep learning algorithms require huge computational and memory resources, making them difficult to achieve effective edge deployment. In order to solve this difficulty with practical industrial significance, this paper proposes an online knowledge distillation framework for machine health prognosis. Within this framework, the learned knowledge of complex networks can be distilled to simple networks that can be deployed on edge devices in sites. Specifically, the response-based knowledge distillation module, feature-based knowledge distillation module, and relation-based knowledge distillation module are respectively designed to achieve effective information transmission from different levels. Furthermore, the inherent differences between simple and complex networks have been fully considered for their impact on the efficiency of knowledge distillation, and an adaptive mutual learning strategy has been contrapuntally proposed to address this limitation. Multiple online knowledge distillation experiments were conducted on two different sets of run-to-failure datasets of mechanical key components with different pairs of complex and simple networks to verify the effectiveness of the proposed framework. The experimental results show that the simple student-networks can effectively improve prediction performance after receiving knowledge distillation from the complex teacher-networks, providing a new solution for machine health prognosis under the premise of edge deployment.

The Industrial Internet of Things (IIoT) greatly facilitates prognostics and health management of complex industrial systems, wherein the vast amount of real-time data from the IIoT improves intelligent predictive maintenance of industrial systems. When processing industrial IoT data across devices, traditional subdomain adaptation-based methods ignore the local similarities across domains. Also, if fault classes are used to define subdomains, these methods may not be applicable when the target domain is unlabeled or has limited labels. To address the above challenges, a Graph-embedded Subdomain Adaptation Network (GSAN)-based approach is proposed to predict the remaining useful life under different machines in IIoT. Specifically, a manifold subdomain representation is established by manifold learning and local manifold discrepancies between each pair of manifold subdomains with the highest similarity are minimized. To maintain a divisible margin for each manifold, a self-supervised intra-manifold regularization module is developed. An extensive evaluation of six transfer scenarios is performed, and the experimental results show that GSAN can achieve more significant outcomes. This can provide some guidance for future work on prognostics across devices and subdomains.

Fault detection or anomaly detection relies heavily on learning from datasets where only normal samples are available, resulting in the emergence of numerous one-class classification (OCC) methods. However, learning discriminative deep representatives with good generalization from cross-domain positive samples remains challenging. Therefore, this work proposes an end-to-end framework, deep transfer one-class classification (DTOCC) for unsupervised fault detection, which combines adversarial generative OCC and distribution alignment from the perspective of manifold learning. Specifically, pseudo-negative samples are generated outside the positive manifold, facilitating the model to learn discrimination with respect to normal and anomaly. Further, cross-domain positive samples are aligned in log-Euclidean manifold space to enhance representation learning. Then, we provide the specific implementations for fault detection and validate its superiority through case studies on multiclass and run-to-failure datasets, simulating both offline and online scenarios.

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.

The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semisupervised graph convolution deep belief network algorithm is proposed in this article. Specifically, the labeled and unlabeled samples are first employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss. Finally, the local and discriminative objective functions are reconstructed under the semisupervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66 % accuracy with only 10 % of training labeled data, which indicates that it is a promising semisupervised intelligent fault diagnosis method.