The vibration signals for offshore wind-turbine high-speed bearings are often contaminated with noises due to complex environmental and structural loads, which increase the difficulty of fault detection and diagnosis. In view of this problem, we propose a fault-diagnosis strategy with good noise immunity in this paper by integrating the two-dimensional convolutional neural network (2DCNN) with random forest (RF), which is supposed to utilize both CNN’s automatic feature-extraction capability and the robust discrimination performance of RF classifiers. More specifically, the raw 1D time-domain bearing-vibration signals are transformed into 2D grayscale images at first, which are then fed to the 2DCNN-RF model for fault diagnosis. At the same time, three procedures, including exponential linear unit (ELU), batch normalization (BN), and dropout, are introduced in the model to improve feature-extraction performance and the noise immune capability. In addition, when the 2DCNN feature extractor is trained, the obtained feature vectors are passed to the RF classifier to improve the classification accuracy and generalization ability of the model. The experimental results show that the diagnostic accuracy of the 2DCNN-RF model could achieve 99.548% on the CWRU high-speed bearing dataset, which outperforms the standard CNN and other standard machine-learning and deep-learning algorithms. Furthermore, when the vibration signals are polluted with noises, the 2DCNN-RF model, without retraining the model or any denoising process, still achieves satisfying performance with higher accuracy than the other methods.

Bearing fault diagnosis has evolved from machine learning to deep learning, addressing the issues of performance degradation in deep learning networks and the potential loss of key feature information. This paper proposes a fault diagnosis method for rolling bearing faults based on ICEEMDAN combined with the Hilbert transform (ICEEMDAN-Hilbert) and a residual network (ResNet). Firstly, the collected fault vibration signals are classified as fault samples and randomly sampled with a fixed length. The IMF components obtained by decomposing the bearing fault vibration signals using ICEEMDAN are able to maximize the restoration of fault vibrations. Then, the IMF components are transformed from one-dimensional time-domain signals to two-dimensional time-frequency domain images using Hilbert transformation. The RGB color images can be directly used in deep learning models without the need for manual labeling of a large amount of data, thereby avoiding the loss of key feature information. The ResNet network incorporates the attention mechanism (CBAM) structure for the precise extraction of fault features, enabling a more detailed classification of fault features. Additionally, the residual network effectively addresses the problem of performance degradation in multi-layer network models. Finally, transfer learning is applied in the deep learning network by freezing the training layer parameters and training the fully connected layer. This effectively solves the problem of insufficient data in real operating conditions, which hinders deep training of the model, while also reducing the training time. By combining the ResNet network with the convolutional block attention module (CBAM) structure, the model completes the recognition and training of time-frequency images for rolling bearing faults. The results demonstrate that the ResNet with CBAM model has strong fault feature extraction capabilities, achieving higher accuracy, 7–12% higher than other conventional network models, and exhibiting superior diagnostic performance compared to other deep learning models.

The LNG marine loading arm is a critical component for transferring LNG from vessels to onshore receiving stations. However, currently, the operation of LNG marine loading arms is still faced with issues such as a slow docking speed and significant environmental impacts from waves and wind. In this paper, we propose a set of feasible improvement schemes for the traditional LNG marine loading arm based on the theory of inventive problem solving (TRIZ). We utilized tools such as functional models, causal chain analysis, contradiction analysis, and Su-Field model analysis to develop these schemes. Our proposed improvements include using machine vision and automatic control to replace manual work, conducting finite element analysis and topology optimization of the LNG marine loading arm to improve its structure, and innovating the design of the end structure of the LNG discharge arm.

Liquified natural gas (LNG) manipulator arms have been widely used in natural gas transportation. However, the automatic docking technology of LNG manipulator arms has not yet been realized. The first step of automatic docking is to identify and locate the target and estimate its pose. This work proposes a petroleum pipeline interface recognition and pose judgment method based on binocular stereo vision technology for the automatic docking of LNG manipulator arms. The proposed method has three main steps, including target detection, 3D information acquisition, and plane fitting. First, the target petroleum pipeline interface is segmented by using a color mask. Then, color space and Hu moment are used to obtain the pixel coordinates of the contour and center of the target petroleum pipeline interface. The semi-global block matching (SGBM) algorithm is used for stereo matching to obtain the depth information of an image. Finally, a plane fitting and center point estimation method based on a random sample consensus (RANSAC) algorithm is proposed. This work performs a measurement accuracy verification experiment to verify the accuracy of the proposed method. The experimental results show that the distance measurement error is not more than 1% and the angle measurement error is less than one degree. The measurement accuracy of the method meets the requirements of subsequent automatic docking, which proves the feasibility of the proposed method and provides data support for the subsequent automatic docking of manipulator arms.

With the growth of global liquefied natural gas (LNG) demand, automation technology has become a key trend to improve the efficiency and safety of LNG handling. In this study, a novel automatic docking system is proposed which adopts a staged docking strategy based on a monocular camera for positioning and combines ultrasonic sensors to achieve multi-stage optimization in the fine docking stage. In the coarse docking stage, the system acquires flange image data through the monocular camera, calculates 3D coordinates based on geometric feature extraction and coordinate transformation, and completes the preliminary target localization and fast approach; in the fine docking stage, the ultrasonic sensor is used to measure the multidirectional distance deviation, and the fusion of the monocular data is used to make dynamic adjustments to achieve high-precision alignment and localization. Simulation and experimental verification show that the system has good robustness in complex environments, such as wind and waves, and can achieve docking accuracy within 3 mm, which is better than the traditional manual docking method. This study provides a practical solution for automated docking of LNG loading arms, which can significantly improve the efficiency and safety of LNG loading and unloading operations.