Accurate detection of steel surface defects is of great significance for ensuring product quality and production safety. However, existing detection models still suffer from insufficient accuracy and poor robustness when facing practical industrial scenarios characterized by complex defect morphologies, diverse scales, blurred boundaries, and strong background interference. To address this, this paper proposes an improved YOLO11 detection model – DSP-YOLO, aimed at enhancing the comprehensive per-formance of steel surface defect detection. We design a Dynamic Attention-mixed Normalized Block, which introduces multi-scale direction-aware dynamic depthwise convolution and channel interaction mechanisms to strengthen the flexibility and ex-pressive power of feature extraction; propose a Semantic Context Fusion Module that integrates semantic awareness and context-guided strategies to effectively improve feature fusion effectiveness under complex textured backgrounds; additionally, intro-duce a lightweight asymmetric pinwheel-shaped convolution structure to significantly enhance the receptive field and directional modeling capability while maintaining low computational cost. Experimental results on two typical steel defect datasets, NEU-DET and GC10-DET, show that the proposed method achieved 2.5% and 2.6% improvements in mAP@0.5 respectively, surpassing existing mainstream YOLO models in terms of accuracy, efficiency, and generalization ability, demonstrating good potential for in-dustrial applications.

The vibration signals of faulty bearings contain rich feature information in both the time and frequency domains. Effectively leveraging this information is crucial, especially when addressing imbalanced bearing fault datasets, as it can significantly enhance the performance of fault diagnosis models. However, existing GAN models and diagnostic methods do not fully exploit these domain-specific features. To overcome this limitation, a novel fault diagnosis method is proposed, based on the Adaptive Wavelet-Like Transform Generative Adversarial Network (AWLT-GAN) and ensemble learning. In the first stage, AWLT-GAN is used to balance the bearing fault dataset by integrating time- and frequency-domain feature information. AWLT-GAN embeds an adaptive wavelet-like transform neural network into the generator as an adaptive layer and employs a dual-discriminator architecture. This design allows the network to simultaneously learn fault characteristics from both domains within a single training session, enhancing the quality of the synthetic fault data. Next, an ensemble learning approach is applied, combining time- and frequency-domain models, with the final classification determined through a soft voting mechanism. Experimental results demonstrate that the vibration signals generated by AWLT-GAN effectively replicate the feature distribution of real data, confirming its high performance. The fault diagnosis model, developed using these high-quality synthetic samples, accurately captures fault characteristics embedded in both the time and frequency domains, resulting in enhanced diagnostic performance. The proposed approach not only addresses the imbalance in bearing fault datasets but also significantly improves diagnostic accuracy.

Smoking in oilfield operation areas poses a severe risk of fire and explosion accidents, threatening production safety, workers’ lives, and the surrounding ecological environment. Such behavior represents a typical preventable unsafe human action. Detecting smoking behaviors among oilfield workers can fundamentally prevent such safety incidents. To address the challenges of low detection accuracy for small objects and frequent missed or false detections under extreme industrial environments, this paper proposes a GCEA-YOLO network based on YOLOv11 for smoking behavior detection. First, a CSP-EDLAN module is introduced to enhance fine-grained feature learning. Second, to reduce model complexity while preserving critical spatial information, an ADown module is incorporated. Third, an enhanced feature fusion module is integrated to achieve effective multiscale feature aggregation. Finally, an EfficientHead module is employed to generate high-precision and lightweight detection results. The experimental results demonstrate that, compared with YOLOv11n, GCEA-YOLO achieves improvements of 20.8% in precision, 6.9% in recall, and 15.1% in mean average precision (mAP). Overall, GCEA-YOLO significantly outperforms YOLOv11n.

The picking-head drivetrain is a critical transmission component of cotton harvesters, and its fault condition monitoring and diagnosis are essential for ensuring stable and reliable operation of the equipment. In practical engineering applications, diagnostic models for picking-head drivetrains are typically initialized using data collected under a limited number of representative operating conditions. Although sufficient fault samples can often be obtained during the initial training stage, the coverage of operating conditions is inherently restricted. As the model is deployed and used in the field, fault samples collected under new operating conditions are gradually acquired in a stage-wise manner. How to stably update the diagnostic model while the operating-condition coverage continuously expands, and how to avoid performance degradation and catastrophic forgetting, remain critical challenges. To address these issues, this paper proposes a continual learning method, termed DP-CL (Disentangled Prototype-Driven Continual Learning), for fault diagnosis of cotton harvester picking-head drivetrains under gradually expanding operating conditions. The proposed method is built upon an explicit disentanglement of condition-invariant features and condition-specific features. Within a unified framework, three types of structured prototypes, including class prototypes, condition prototypes, and condition-aware class prototypes, are constructed to form a multi-level representation hierarchy. A prototype-driven structured update mechanism is then employed to impose stable constraints on fault-discriminative semantics across different operating conditions. In addition, an operating-condition similarity measurement based on condition-specific features is introduced, based on which a proportion-adaptive sample selection strategy is designed. This strategy enables controlled knowledge transfer and preservation of discriminative structures during multi-stage model updates. Experimental results obtained under a laboratory-constructed cumulative operating-condition expansion scenario demonstrate that the proposed method achieves superior performance in terms of overall performance retention, cross-stage stability, and resistance to performance degradation. Moreover, as the number of operating conditions increases, the proposed method maintains a relatively smooth performance variation trend, while preserving clear class structures and a controllable level of confusion. These results validate the effectiveness of the proposed approach for stable fault diagnosis under expanding operating-condition coverage.

Cross-condition fault diagnosis of cotton harvester picking-head drivetrains remains challenging due to significant distribution discrepancies in vibration signals under different operating conditions. Existing transfer learning approaches predominantly focus on domain-invariant features while failing to sufficiently exploit domain-specific information and the structural constraints embedded in target-domain normal samples, which often leads to unstable diagnostic performance across conditions. To address this issue, this paper proposes a prototype-guided dual-feature transfer learning method termed Proto-DISFNet (Prototype-guided Domain-Invariant and Domain-Specific Feature Network). The proposed method explicitly disentangles domain-invariant and domain-specific features to alleviate the impact of operating condition variations. High-confidence pseudo-labeled samples, selected through a filtering strategy, are utilized to construct class prototypes in the target domain, thereby enhancing semantic consistency and structural awareness in the feature space. In addition, a stage-wise training strategy is introduced to coordinate multi-task optimization, which improves training stability and overall adaptability under representative complex engineering operating conditions. Experiments conducted on three vibration datasets, JNU, THU, and CHPH-FETB, demonstrate that Proto-DISFNet achieves stable and competitive cross-condition diagnostic performance under varying degrees of domain shift and operating conditions. These results indicate the engineering relevance and potential applicability of the proposed method for fault diagnosis of cotton harvester picking-head drivetrains.

To address the drawbacks of traditional convolutional neural network-based rolling bearing fault diagnosis techniques, including poor feature extraction, low diagnostic accuracy, and poor generalization capability, a dual-channel rolling bearing fault diagnosis model based on VMD-BiGRU and GADF-ResNet-CBAM was proposed. Variational mode decomposition (VMD) was used to first break down and reconstruct the original vibration signal. The rebuilt signal was then input into a bidirectional gated recurrent unit (BiGRU) network in order to extract temporal information. Second, the Gramian angular difference field (GADF) transformed the one-dimensional vibration signal into a two-dimensional picture. This image was then fed into a residual network that was merged with the convolutional block attention module (CBAM) in order to extract spatial characteristics. After concatenating and fusing the data from the two channels, Softmax was finally employed at the output layer to classify different types of faults. The Case Western Reserve University (CWRU) bearing dataset and a self-collected independent dataset from the Xinjiang University experimental rig were utilized for validation. The model achieved diagnosis accuracies of 99.39% and 99.58%, respectively. These results demonstrate the robustness and practical applicability of the proposed method on data acquired from distinct hardware sources and experimental environments, outperforming alternative approaches.