Oil–gas–water three-phase flow process exhibits randomness and transience. Under varying flow conditions and environmental factors, the process presents a dual challenge in state monitoring: data distribution discrepancies between source and target domains, along with the presence of unknown states in the target domain. Therefore, a cross-domain open-set state monitoring method based on multigrained adversarial network with hierarchical attribute causality (MANHAC) is presented in this work. A domain adversarial architecture is designed to distinguish unknown from known target flow states, in which the weighted thresholding method based on information entropy can adjust the decision boundary adaptively. Besides, to mitigate conditional distribution mismatch and negative transfer caused by forced global domain alignment, MANHAC introduces hierarchical causal attributes for describing different flow states, where attribute features are influenced by upstream cause attribute and optimized through competition with multiple discriminators. The proposed MANHAC employs a dual-alignment mechanism to achieve both global and fine-grained domain adaptation, which can effectively carry out cross-domain open set state identification. More importantly, it can describe unknown states by attribute vectors, providing more meaningful monitoring information. Dynamic experiments of three-phase flow demonstrate its effectiveness and superiority.

Transfer learning has been widely applied to intelligent fault diagnosis to address the challenge of insufficient labeled data. However, the efficacy of existing semi-supervised domain adaptation (SSDA) methods is often constrained by their critical dependence on pseudo-label quality and the adoption of indiscriminate global alignment strategies. To address the negative transfer induced by these limitations, a deep gated network (DGN) for targeted transfer learning is proposed in this article. First, an anchor-guided manifold clustering (AGMC) method is developed to generate high-quality pseudo-labels by exploiting both the local manifold structure and anchor supervision in the target domain. Subsequently, a feature extractor is constructed to learn discriminative representations from the source domain, integrate high-quality supervisory information from the target domain, and impose constraints on the feature space. Furthermore, a gated domain alignment strategy is designed to achieve precise class-level transfer. This strategy incorporates an integrated gating mechanism to selectively filter out domain-specific features while employing the local maximum mean discrepancy (LMMD) to align the conditional distributions across domains. Finally, the effectiveness and superiority of the proposed method are validated through transfer experiments on both cross-machine bearing and cross-condition two-phase flow datasets.

The accurate prediction of bearing remaining useful life (RUL) is crucial for industrial safety and cost reduction. However, existing deep learning methods often lack interpretability and struggle to integrate local-global and structural-temporal information, limiting their effectiveness. To address this issue, an observable and controllable RUL prediction method is proposed, which visualizes the bearing degradation process through spatio-temporal dynamic graph (STDG), and fully extracts structural information and temporal features to achieve precise RUL prediction. First, canonical variable fluctuation analysis (CVFA) is employed to maximize past-future data correlation, enabling information distillation by analyzing static deviations and dynamic fluctuations. Second, restraint graph attention network (RGAT) is proposed to extract local structural information, generating dynamic factors that feedback into the spatio-temporal static graph (STSG) to construct the STDG for observation and control of experts. Thirdly, to address degradation variability and long sequence dependency, the reversible instance normalization Transformer (RevIN-Former) explores the temporal evolution of the STDG from a global perspective, achieving accurate RUL prediction. Finally, the effectiveness of the proposed method is validated through three accelerated life experiments on bearings, which reduces the RUL prediction error by 88% compared to other methods.

Gas–liquid two-phase flow is a complex dynamic and nonlinear process that is widely encountered in many process industries. Accurate flow state identification is crucial for ensuring operation safety and economic benefits. However, obtaining training samples for certain flow states can be difficult due to safety requirements and high costs. Therefore, a zero-shot learning (ZSL) based flow state identification strategy is proposed from the perspective of attribute description and attribute transfer, in which the common attribute space is constructed by the semantic description of flow state categories. The attribute-relevant features are extracted by the proposed supervised deep slow and steady feature analysis (SD-S$^{\mathbf{2}}$FA) under the supervision of attributes. In SD-S$^{\mathbf{2}}$FA, an extended Siamese network is designed to extract slow and steady features (S$^{\mathbf{2}}$Fs), in which three 1D convolutional neural networks (1D-CNN) represent the nonlinear feature embedding function, and the Siamese architecture can capture the long-term temporal coherence. Since the state attributes are shared by all the flow states, the identification for unseen flow states can be realized by attribute prediction and attribute transfer. The effectiveness and superiority of the proposed method is demonstrated through the gas–liquid two-phase flow experiment.

Industrial ultrasonic tomography (UT) possesses unique advantages in multiphase medium imaging and has received broad attention. In this article, a novel transmission/reflection dual-mode image reconstruction algorithm based on information fusion is proposed. The transmissive attenuation and reflective time-delay information are both integrated into an improved Lagrange framework with the weighted least square transformation of objective function, which is then solved by a pair of coupled preconditioned gradient approaches. Experiment results show that the proposed algorithm performs better than existing image fusion strategies in terms of accuracy (average relative error 0.456, average correlation coefficient 0.870) and robustness (average standard derivation of relative error and correlation coefficient 0.056 and 0.041). Accordingly, the dual-mode UT approach is proved feasible to provide more accurate image of biphasic medium distribution.

Gas-water two-phase flow has typically stable flow statuses and constantly changing transition flow statuses. Accurate identification and real-time monitoring of flow status are conducive to the in-depth study of two-phase flow and the safe operation of industrial process. A monitoring strategy based on sparse local Fisher discriminant analysis (SLFDA) is proposed in this article. First, multisensor signals are obtained to reflect flow process information. Second, the least absolute shrinkage and selection operator is used to find the sparse discriminant directions to determine the key variables relevant to the flow process from multiple sensor signals. Then, the weight coefficient matrixes of SLFDA keep the original structure of the same flow status data and make the data of different flow statuses more separated, which distinguish different flow statuses to the maximum extent. Finally, two monitoring indexes including the discriminant index and the stability index are established to analyze the dynamic flow process, which enable a concurrent monitoring of both flow evolution and instability to realize fine-scale description of flow process. SLFDA can monitor various flow statuses through only one projection discriminant matrix by transforming high-dimensional signals into features representing the flow characteristics, which avoids model traversal and improves monitoring efficiency. Further study of flow status features provides meaningful physical interpretation and in-depth process analysis with consideration of actual flow process. The application on the data of gas-water two-phase flow in horizontal pipe demonstrates the feasibility and efficacy of the method.

A shape-based statistical inversion method is proposed for Electrical Impedance Tomography (EIT) and Ultrasound Reflection Tomography (URT) dual-modality imaging. It is promising to improve the imaging accuracy in inclusion detection problems. The proposed image reconstruction method is based on the statistical shape inversion framework. The likelihood function is derived from EIT and URT forward models. The prior distribution is constructed using the Markov random field (MRF) prior. The measurement uncertainty is modeled by conditional error model method. The statistical shape inversion problem is solved by the Maximum a posterior (MAP) method with conventional error model. A set of numerical and experimental tests are carried out to evaluate the performance of the proposed method. The results show that the proposed EIT/URT dual-modality imaging method has obvious improvement in imaging accuracy compared to the traditional single-modality EIT and URT methods.

A statistical shape-constrained reconstruction (SSCR) framework is presented to incorporate the statistical prior information of human lung shapes for lung electrical impedance tomography. The prior information is extracted from 8000 chest-computed tomography scans across 800 patients. The reconstruction framework is implemented with two approaches-a one-step SSCR and an iterative SSCR in lung imaging. The one-step SSCR provides fast and high accurate reconstructions of healthy lungs, whereas the iterative SSCR allows to simultaneously estimate the pre-injured lung and the injury lung part. The approaches are evaluated with the simulated examples of thorax imaging and also with the experimental data from a laboratory setting, with difference imaging considered in both the approaches. It is demonstrated that the accuracy of lung shape reconstruction is significantly improved. In addition, the proposed approaches are proved to be robust against measurement noise, modeling error caused by inaccurately known domain boundary, and the selection of the regularization parameters.