Recently, research on the fault diagnosis of rotating machinery, especially for the compound or unknown cases, has drawn increasing attention. Some advanced learning-based approaches have achieved good fault diagnosis performance to some degree. However, in practical applications, how to utilize prior knowledge as fully as possible for fault diagnosis with constraints of limited expert interaction remains an open issue. In this brief, a fault diagnosis methodology of rotating machinery with limited expert interaction is proposed. With related feature extraction techniques, a novel multicriteria active learning (MCAL) query strategy is designed to select the relatively valuable samples for annotation. In addition, the broad learning system (BLS) is exploited to achieve fast incrementally updating or retrain procedures with high diagnostic accuracy in different diagnosis scenarios. Several experiments are conducted on a real-world rotating machinery fault diagnosis (RMFD) experimental platform. Compared with other existing advanced approaches, the diagnosis performance of the proposal shows high stability and flexibility. The annotation cost of experts is also significantly reduced, which makes the proposal more suitable for dealing with practical problems.

Operational safety and reliability have become increasingly paramount in large and complex rotating machinery in industrial manufacturing. Recent strides in data-driven fault diagnosis have significantly advanced the fields of machinery health assessment, fault identification, and prediction. This review delves into the existing literature on fault diagnosis technology for rotating machinery, categorizing the various diagnostic approaches. It further examines the evolution of fault diagnosis technology based on dimensionless indices and elucidates the five latest progressions of foundational theories: the design and optimization of dimensionless indices, extraction and features of mutual dimensionless indices, fault diagnosis technology based on dimensionless indices and artificial immune system, fusion technology of dimensionless indices in time and frequency domains, and datasets. The applications of fault diagnosis technology based on dimensionless indices in the petrochemical industry, high-speed railway, shipping and aerospace are discussed. Future directions for research are outlined across six key areas: more excellent dimensionless indices, rapid and efficient detectors, fault analysis for small-sample fault events, theoretical systems of time–frequency domain fusion diagnosis, fault diagnosis with standardized test database, and expert knowledge base. This comprehensive review bridges the research gap by systematically examining the roles and applications of dimensionless indices in the fault diagnosis of rotating machinery.

The prediction of the remaining useful life of rolling bearings is a pivotal issue in industrial production. A crucial approach to tackling this issue involves transforming vibration signals into health indicators (HI) to aid model training. This paper presents an end-to-end HI construction method, vector quantised variational autoencoder (VQ-VAE), which addresses the need for dimensionality reduction of latent variables in traditional unsupervised learning methods such as autoencoder. Moreover, concerning the inadequacy of traditional statistical metrics in reflecting curve fluctuations accurately, two novel statistical metrics, mean absolute distance (MAD) and mean variance (MV), are introduced. These metrics accurately depict the fluctuation patterns in the curves, thereby indicating the model’s accuracy in discerning similar features. On the PMH2012 dataset, methods employing VQ-VAE for label construction achieved lower values for MAD and MV. Furthermore, the ASTCN prediction model trained with VQ-VAE labels demonstrated commendable performance, attaining the lowest values for MAD and MV.

Accurate prediction of the remaining useful life (RUL) of rolling bearings is crucial in industrial production, yet existing models often struggle with limited generalization capabilities due to their inability to fully process all vibration signal patterns. We introduce a novel multi-input autoregressive model to address this challenge in RUL prediction for bearings. Our approach uniquely integrates vibration signals with previously predicted RUL values, employing feature fusion to output current window RUL values. Through autoregressive iterations, the model attains a global receptive field, effectively overcoming the limitations in generalization. Furthermore, we innovatively incorporate a segmentation method and multiple training iterations to mitigate error accumulation in autoregressive models. Empirical evaluation on the PMH2012 dataset demonstrates that our model, compared to other backbone networks using similar autoregressive approaches, achieves significantly lower root mean square error (RMSE) and Score. Notably, it outperforms traditional autoregressive models that use label values as inputs and non-autoregressive networks, showing superior generalization abilities with a marked lead in RMSE and Score metrics.

Accurate and timely diagnosis of rice leaf diseases is crucial for smart agriculture leveraging vision sensors. However, existing lightweight convolutional neural networks (CNNs) often struggle in complex field environments, where small lesions, cluttered backgrounds, and varying illumination complicate recognition. This paper presents I-GhostNetV3, an incrementally improved GhostNetV3-based network for RGB rice leaf disease recognition. I-GhostNetV3 introduces two modular enhancements with controlled overhead: (1) Adaptive Parallel Attention (APA), which integrates edge-guided spatial and channel cues and is selectively inserted to enhance lesion-related representations (at the cost of additional computation), and (2) Fusion Coordinate-Channel Attention (FCCA), a near-neutral SE replacement that enables efficient spatial–channel feature fusion to suppress background interference. Experiments on the Rice Leaf Bacterial and Fungal Disease (RLBF) dataset show that I-GhostNetV3 achieves 90.02% Top-1 accuracy with 1.831 million parameters and 248.694 million FLOPs, outperforming MobileNetV2 and EfficientNet-B0 under our experimental setup while remaining compact relative to the original GhostNetV3. In addition, evaluation on PlantVillage-Corn serves as a supplementary transfer sanity check; further validation on independent real-field target domains and on-device profiling will be explored in future work. These results indicate that I-GhostNetV3 is a promising efficient backbone for future edge deployment in precision agriculture.

Recently, multiantenna spectrum sensing using deep neural network has received much attention. However, most of them are not explicitly formulated to account for noncircular sources. In practice, the primary users' signals may come from either circular or noncircular sources, or both. We are, therefore, motivated to investigate the multiantenna spectrum sensing (MSS) problem for hybrid circular and noncircular source signals in cognitive radio. Specifically, we first introduce the augmented sample covariance matrix (ASCM) to exploit the additional statistical information given by the noncircular property. Then, based on the ASCM, a simple convolutional neural network (CNN) structure is designed to extract the features embedded in the ASCM. Simulation results reveal that the proposed method is capable of providing performance improvement over conventional MSS methods regardless of the spatially white/correlated noise.