In order to improve the accuracy of bearing fault diagnosis under a small sample, variable load, and noise conditions, a new fault diagnosis method based on an image information fusion and Vision Transformer (ViT) transfer learning model is proposed in this paper. Firstly, the method applies continuous wavelet transform (CWT), Gramian angular summation field (GASF), and Gramian angular difference field (GADF) to the time series data, and generates three grayscale images. Then, the generated three grayscale images are merged into an information fusion image (IFI) using image processing techniques. Finally, the obtained IFIs are fed into the advanced ViT model and trained based on transfer learning. In order to verify the effectiveness and superiority of the proposed method, the rolling bearing dataset from Case Western Reserve University (CWRU) is used to carry out experimental studies under different working conditions. Experimental results show that the method proposed in this paper is superior to other traditional methods in terms of accuracy, and the effect of ViT model based on transfer learning (TLViT) training is better than that of the Resnet50 model based on transfer learning training (TLResnet50) under variable loads and small sample conditions. In addition, the experimental results also prove that the IFI with multiple image information has better anti-noise ability than the single information image. Therefore, the method proposed in this paper can improve the accuracy of bearing fault diagnosis under small sample, variable load and noise conditions, and provide a new method for bearing fault diagnosis.

Structural health monitoring and fault diagnosis are important scientific issues in mechanical engineering, civil engineering, and other disciplines. The basic premise of structural health work is to be able to accurately diagnose the fault in the structure. Therefore, the accurate fault diagnosis of structure can not only ensure the safe operation of mechanical equipment and the safe use of civil construction, but also ensure the safety of people’s lives and property. In order to improve the accuracy fault diagnosis of frame structure under noise conditions, the existing Convolutional Neural Network with Training Interference (TICNN) model is improved, and a new convolutional neural network model with strong noise resistance is proposed. In order to verify THE superiority of the proposed improved TICNN in anti-noise, comparative experiments are carried out by using TICNN, One Dimensional Convolution Neural Network (1DCNN) and First Layer Wide Convolution Kernel Deep Convolution Neural Network (WDCNN). The experimental results show that the improved TICNN has the best anti-noise ability. Based on the improved TICNN, the fault diagnosis experiment of a four-story steel structure model is carried out. The experimental results show that the improved TICNN can obtain high diagnostic accuracy under strong noise conditions, which verifies the advantages of the improved TICNN.

Accurate damage location diagnosis of frame structures is of great significance to the judgment of damage degree and subsequent maintenance of frame structures. However, the similarity characteristics of vibration data at different damage locations and noise interference bring great challenges. In order to overcome the above problems and realize accurate damage location diagnosis of the frame structure, the existing convolutional neural network with training interference (TICNN) is improved in this paper, and a high-precision neural network model named convolutional neural network based on Inception (BICNN) for fault diagnosis with strong anti-noise ability is proposed by adding the Inception module to TICNN. In order to effectively avoid the overall misjudgment problem caused by using single sensor data for damage location diagnosis, an integrated damage location diagnosis method is proposed. Taking the four-story steel frame model of the University of British Columbia as the research object, the method proposed in this paper is tested and compared with other methods. The experimental results show that the diagnosis accuracy of the proposed method is 97.38%, which is higher than other methods; at the same time, it has greater advantages in noise resistance. Therefore, the method proposed in this paper not only has high accuracy, but also has strong anti-noise ability, which can solve the problem of accurate damage location diagnosis of complex frame structures under a strong noise environment.

Pointer meters have been widely used in industrial field due to their strong stability; it is an important issue to be able to accurately read the meter. At present, patrol robots with computer vision function are often used to detect and read meters in some situations that are not suitable for manual reading of the meter. However, existing object detection algorithms are often misread and miss detection due to factors such as lighting, shooting angles, and complex background environments. To address these problems, this paper designs a YOLOv4-Tiny-based pointer meter detection model named pointer meter detection-YOLO (PMD-YOLO) for the goal of practical applications. Firstly, to reduce weight of the model and ensure the accuracy of object detection, a feature extraction network named GhostNet with a channel attention mechanism is implemented in YOLOv4-Tiny. Then, to enhance feature extraction ability of small- and medium-sized targets, an improved receptive field block (RFB) module is added after the backbone network, and a convolutional block attention module (CBAM) is introduced into the feature pyramid network (FPN). Finally, the FPN is optimized to improve the feature utilization, which further improves the detection accuracy. In order to verify the effectiveness and superiority of the PMD-YOLO proposed in this paper, the PMD-YOLO is used for experimental research on the constructed dataset of the pointer meter, and the target detection algorithms such as Faster region convolutional neural network (RCNN), YOLOv4, YOLOv4-Tiny, and YOLOv5-s are compared under the same conditions. The experimental results show that the mean average precision of the PMD-YOLO is 97.82%, which is significantly higher than the above algorithms. The weight of the PMD-YOLO is 9.38 M, which is significantly lower than the above algorithms. Therefore, the PMD-YOLO not only has high detection accuracy, but can also reduce the weight of the model and can meet the requirements of practical applications.

The complex application environments of frame structures and the similar vibration signals between different locations make it difficult to accurately diagnose damage using traditional methods. Based on modifying the parameters and configuration of the convolution neural network with training interference (TICNN), this paper proposes a new model for damage diagnosis of frame structures by implanting a squeeze-and-excitation neural network (SENet) and Res2Net modules. Taking the frame structure model from the University of British Columbia as the research object, the proposed damage diagnosis model was used to diagnose its damage type. The proposed new model was compared with other models in terms of accuracy and anti-noise ability. The experimental results show that the accuracy of the proposed model was 99.44% when the training epoch was 30 and 99.78% when training epoch was 100. It is superior to other similar models in terms of convergence speed and accuracy. At the same time, the proposed model also has an excellent advantage in anti-noise ability. Therefore, the proposed damage diagnosis model has the advantages of fast convergence and higher damage diagnosis accuracy under a strong noise environment. It can realize the accurate damage diagnosis of structural frames.

Aiming at a problem that it is difficult for accurately obtaining the mathematical model of a gas temperature for a high-speed, high-temperature heat-airflow test system, a grey box modeling method combining first principle of modeling, recursive least squares identification with auxiliary variable and correlation analysis is proposed, and the precise mathematical model of gas temperature is established. Firstly, the preliminary mathematical model of gas temperature is obtained by using the first principle modeling method, and the dynamic behaviors of the system are analyzed; the prior knowledge of the system is obtained, and the parameters that need to be identified are pointed out. Secondly, the basic principles of recursive least square identification with auxiliary variables and correlation analysis are introduced, and the uncertain parameters of the gas temperature model are identified by using the introduced methods. Finally, the precise mathematical model of gas temperature is obtained by simulation and experimental research. The research results show that the mathematical model of gas temperature established by the grey box modeling method put forward in this article has satisfactory accuracy.