Rotating machinery fault diagnosis is essential for ensuring the reliability of industrial assets in IIoT-enabled manufacturing environments, where the high variability of operating conditions has driven the widespread adoption of multi-sensor fusion (MSF) to extract discriminative fault features. However, harsh IIoT environments frequently cause partial sensor failures or communication interruptions, under which conventional multi-sensor fusion systems often suffer significant performance degradation or even complete fusion failure. To address this issue, a Multi-task Multi-input Multi-scale Mixture-of-Experts (M4oE) framework is proposed, in which an independent diagnosis task is constructed for each sensor signal, ensuring that any individual sensor stream, representing the most extreme case of single-sensor availability, can independently perform diagnostic inference. First, a multi-scale mixture-of-experts feature extraction scheme is proposed, in which both the task-specific experts and each shared expert are implemented using the proposed Omni-scale Dilated Convolution Neural Network (OSD-CNN) architecture. Subsequently, task-specific features and shared features are integrated through multi-level feature fusion and fed into task-specific decoders to generate diagnostic results. Since the diagnostic outputs obtained from each sensor have the same identification framework and independent evidence sources, M4oE further employs Dempster-Shafer (D-S) decision-level fusion to enhance the reliability of the overall diagnostic system. Finally, comprehensive evaluations are conducted on three different types of rotating machinery datasets, including pumps, rolling mills, and bogies, to verify the accuracy, robustness, and scalability of the proposed M4oE framework.

Wind power gains more and more attention from all over the world as a clean and renewable energy resource, and accurate prediction of wind speed has become a hot issue. This paper presents a novel incremental forecaster based on phase space reconstruction and broad learning network (BLN) for short-term prediction. First, time delay and embedding dimension which take an essential part in phase space reconstruction are determined by the C–C algorithm. Then, these optimal parameters are input to the BLN trained incrementally. Afterward, forecasting values are given by the output layer of BLN. Data collected from a wind farm is adopted for verifying the efficacy of this proposed model. Furthermore, five commonly used assessment indicators are applied to evaluate predictive performance of different models. Results show that the proposed model has the smallest prediction error, which performs better than the other models at one-step to three-step ahead forecasting, and this strength is attributed to address the problem of local optimum. Furthermore, the proposed model consumes less training time than the other models. Therefore, the proposed model tends to be promising for wind speed prediction of the big data era.

Transfer learning of multi-source domain adaptation seems a promising way for fault diagnosis of roller element bearings under variable working conditions. Data imbalance affects the performance of multi-source domain adaptation greatly and is expected to be solved by GAN. However, GAN-based transfer learning diagnosis models suffer pattern collapse and training instability, leading to unsatisfying diagnosis results in practical engineering. This paper proposes a denoising diffusion multi-source domain adaptation model (DDMDA). The proposed model uses diffusion denoising, which has better performance and is simpler to train than GAN, to generate shifted source domains for solving the data imbalance problem. A new noise prediction structure in diffusion denoising named Utrans-net, is constructed to restore the data distribution in the shifted source domain. Also, a multiple-domain discriminator structure is designed to extract features from multiple source domains to solve the issue of variable working conditions. Advanced models are used in this paper to compare with the proposed model for validation. Experimental demonstrations show that the proposed model is superior to the comparison models with satisfying performance.

Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is [Formula: see text]. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts.