The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.

Remaining useful life (RUL) prediction of industrial equipment is critical for achieving safe operations and optimizing predictive maintenance. To tackle the limitations of poor interpretability, inaccurate predictions, and high computational cost in complex system degradation modeling, this paper proposes SiMBA-PINN, a novel fusion framework that synergizes Physics-Informed Neural Network (PINN) with an enhanced state-space model (SiMBA). The framework achieves dynamic fusion of data-driven features and physical laws through a two-branch synergistic mechanism: the temporal modeling branch combines selective state-space SiMBA with Einstein Fast Fourier Transform (EinFFT)-based spectral mixing to efficiently capture cross-sensor temporal dependencies and degradation trends, while the physics-constraint branch embeds automatically differentiable partial differential equation residuals derived from domain-specific degradation mechanisms, enforcing physical consistency through deep hidden physics modeling. Here, the EinFFT-based spectral mixing leverages frequency-domain interactions to effectively blend the spectral components of multivariate time-series data, thereby enhancing the modeling of cross-sensor dependencies. Meanwhile, deep hidden physics modeling integrates physics-informed partial differential equation (PDE) residuals through differentiable operators, aligning the learned representations with domain-specific dynamics via a constraint-driven loss design. Experimental results from the C-MAPSS dataset confirm that the proposed model significantly outperforms PINN-, Mamba- and attention mechanism-based models, achieving State-of-the-Art RMSE on the most challenging FD004 subset. This physics-aware framework achieves deployable and interpretable RUL prediction by balancing accuracy with linear-time complexity.

In this paper, SEPAM_HRNet, a high-resolution pose-estimation model that incorporates the squeeze-and-excitation and pixel-attention-mask (SEPAM) module is proposed. Feature pyramid extraction, channel attention, and pixel-attention masks are integrated into the SEPAM module, resulting in improved model performance. The construction of the model involves replacing ordinary convolutions with the plug-and-play SEPAM module, which leads to the creation of the SEPAMneck module and SEPAMblock module. To evaluate the model’s performance, the YOGA2022 human yoga poses teaching dataset is presented. This dataset comprises 15,350 images that capture ten basic yoga pose types—Warrior I Pose, Warrior II Pose, Bridge Pose, Downward Dog Pose, Flat Pose, Inclined Plank Pose, Seated Pose, Triangle Pose, Phantom Chair Pose, and Goddess Pose—with a total of five participants. The YOGA2022 dataset serves as a benchmark for evaluating the accuracy of the human pose-estimation model. The experimental results demonstrated that the SEPAM_HRNet model achieved improved accuracy in predicting human keypoints on both the common objects in context (COCO) calibration set and the YOGA2022 calibration set, compared to other state-of-the-art human pose-estimation models with the same image resolution and environment configuration. These findings emphasize the superior performance of the SEPAM_HRNet model.

The mainstream methods for fault diagnosis of ZPW-2000A track circuits are overly complex in extracting features from sequence data, and their feature extraction capability is relatively limited, thus impacting the performance of fault diagnosis. To address this issue, we propose a fault diagnosis model for track circuits, named as convolutional neural network incorporating multi-head self-attention mechanism (CNN-MHSA). On one hand, the local features of sequence data are locally sensed and extracted by the convolutional layer; on the other hand, the global features of sequence data are captured by establishing long-distance dependency relationships through the multi-head self-attention layer. The dual extraction of local and global features enables accurate diagnosis of faults. Finally, we collect 27 fault modes and 2 normal operation modes in the simulation system and conduct a large number of numerical experiments. The experimental results show that the fault diagnosis accuracy of the CNN-MHSA model in this paper can reach 97.45%, which is an improvement of 0.64%, 0.44%, 0.44%, 0.44%, 0.33%, 0.22%, and 0.11% compared with models such as Decision Tree, Random Forest, CatBoost, long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM. It also has obvious advantages in evaluation metrics such as precision, recall, Macro-F1, and Micro-F1, as well as other evaluation metrics. Therefore, the model significantly improves the comprehensive performance of ZPW-2000A track circuit fault diagnosis and can be used as a more effective fault diagnosis method.

Remaining Useful Life (RUL) plays a critical role in prognostics and health management systems. It helps increase reliability and safety for the equipment used in the modern industry. The new idea proposed is the Mamba deep learning model, which aims to find a good balance between predictive performance and computation cost. This paper presents a multimodal RUL prediction model, Cau–BiMamba–LSTM, using causal discovery, a bidirectional Mamba (BiMamba), attention mechanism, and Long Short-Term Memory (LSTM). The framework utilizes maximum information transfer entropy and simple exponential smoothing in building a causal graph model that extracts groups of feature variable groupsLSTM performs long-range dependencies; the attention mechanism dynamically focuses attention according to the temporal context; finally, the bidirectional state space model captures all contextual information over time for a richer insight into underlying data patterns. Tests conducted on the C-MAPSS dataset confirm that this model achieves superior predictive accuracy and robustness. Moreover, the model achieves high predictive performance in very complex, long time–series and provides fast responses.