This article introduces a novel framework for diagnosing faults in rolling bearings. The framework combines digital twin data, transfer learning theory, and an enhanced ConvNext deep learning network model. Its purpose is to address the challenges posed by the limited actual fault data density and inadequate result accuracy in existing research on the detection of rolling bearing faults in rotating mechanical equipment. To begin with, the operational rolling bearing is represented in the digital realm through the utilization of a digital twin model. The simulation data produced by this twin model replace traditional experimental data, effectively creating a substantial volume of well-balanced simulated datasets. Next, improvements are made to the ConvNext network by incorporating an unparameterized attention module called the Similarity Attention Module (SimAM) and an efficient channel attention feature referred to as the Efficient Channel Attention Network (ECA). These enhancements serve to augment the network’s capability for extracting features. Subsequently, the enhanced network model is trained using the source domain dataset. Simultaneously, the trained model is transferred to the target domain bearing using transfer learning techniques. This transfer learning process enables the accurate fault diagnosis of the main bearing to be achieved. Finally, the proposed method’s feasibility is validated, and a comparative analysis is conducted in comparison with similar approaches. The comparative study demonstrates that the proposed method effectively addresses the issue of low mechanical equipment fault data density, leading to improved accuracy in fault detection and classification, along with a certain level of robustness.

Large and expensive mechanical equipment such as wind turbines generally has limited fault datasets from real-world operations for digital model development. This often leads to poor accuracy in implementing a model based on the life prediction. To address this data shortage issue in developing deep learning models, a remaining useful life prediction approach is proposed in this paper, which combines digital twin technology with transfer learning theory and the embedded convolutional long short-term memory (CLSTM) extended model. First, the main bearing of a direct-drive wind turbine is mapped to the digital world using digital twin technology, allowing the fault datasets of main bearings to be generated and thereby ensuring the model is trained sufficiently with a balanced dataset. The CLSTM network then performs convolutional operations on input-to-state and state-to-state transitions, thereby integrating the time dependence and time-frequency characteristics of the data. Meanwhile, transfer learning is used to transfer the trained model to the wind field for real-world fault diagnostics and the life prediction of the main bearings. Finally, the approach is applied to predict the life of the main bearings, and is also compared with other methods of similar type. The results verified that the proposed approach can effectively overcome the low data density of large equipment, greatly improving the accuracy of life prediction.

In industry, accurate remaining useful life (RUL) prediction is critical in improving system reliability and reducing downtime and accident risk. Numerous data-driven RUL prediction approaches have been proposed and achieved impressive performance in RUL prediction. However, most of them are still faced with the dilemma of limited samples, and most of popular transfer learning and domain adaptive methods adopt single-source domain adaptation (DA), ignoring the domain-shift within source domain and failing to fully utilize the multi-condition data. This article proposes a model-data fusion life prediction method based on digital twin (DT) and multi-source regression adversarial domain adaptation (MRADA) to address the aforementioned issues. For data-driven life prediction model, the model-based DT technology among them offers a significant amount of multi-condition training data. The proposed MRADA fully utilizes the benefits of DT simulation data by using intra-group alignment strategy, inter-group alignment strategy, adversarial learning, and regressor alignment strategy to learn domain-invariant features and supervision from multiple sources. The experimental findings demonstrate that the proposed fusion life prediction method can successfully address the issue of small samples and improve the accuracy of rolling bearing life prediction results.

Cattle farming is an important part of the global livestock industry, and cattle body size is the key indicator of livestock growth. However, traditional manual methods for measuring body sizes are not only time-consuming and labor-intensive but also incur significant costs. Meanwhile, automatic measurement techniques are prone to being affected by environmental conditions and the standing postures of livestock. To overcome these challenges, this study proposes a multi-view fusion-driven automatic measurement system for full-attitude cattle body measurements. Outdoors in natural light, three Zed2 cameras were installed covering different views of the channel. Multiple images, including RGB images, depth images, and point clouds, were automatically acquired from multiple views using the YOLOv8n algorithm. The point clouds from different views undergo multiple denoising to become local point clouds of the cattle body. The local point clouds are coarsely and finely aligned to become a complete point cloud of the cattle body. After detecting the 2D key points on the RGB image created by the YOLOv8x-pose algorithm, the 2D key points are mapped onto the 3D cattle body by combining the internal parameters of the camera and the depth values of the corresponding pixels of the depth map. Based on the mapped 3D key points, the body sizes of cows in different poses are automatically measured, including height, length, abdominal circumference, and chest circumference. In addition, support vector machines and Bézier curves are employed to rectify the missing and deformed circumference body sizes caused by environmental effects. The automatic body measurement system measured the height, length, abdominal circumference, and chest circumference of 47 Huaxi Beef Cattle, a breed native to China, and compared the results with manual measurements. The average relative errors were 2.32%, 2.27%, 3.67%, and 5.22%, respectively, when compared with manual measurements, demonstrating the feasibility and accuracy of the system.

One of the key critical technologies in the digital revolution of measurement technology is digital twin. The literature now in publication indicates that the advancement and use of digital twin technology will raise the bar for improvement in the measuring sector. The current literature on the creation and use of digital twin technology is reviewed first, followed by a list of recognized definitions and a summary of the three main categories of digital twin models for easy reference. The main drawbacks of conventional measurement technology in the application process are enumerated here: direct measurement is challenging, measuring multiple parameters at once is challenging, sensors’ influence cannot be disregarded, and the accuracy of measurement results is not satisfactory. To address these issues, this review outlines the benefits and potential uses of digital twin technology in measurement, as well as a summary of six significant contributions. Strong application and robustness, the ability to visualize the process of changing a measurement parameter, simultaneous measurement of many parameters, cheap measurement costs, data security, integrity, high availability, and intelligent measurement are only a few of these features. It is explored where digital twin research in measurement technology is headed in the future. A new digital solution and path for measuring technology development are offered by the digital twin and virtual sensor simulation methods.