Thermal ice storage has gained a lot of interest due to its ability as cold energy storage. However, low thermal conductivity and high supercooling degree have become major issues during thermal cycling. For reducing the cost and making full use of the advantages of the graphene oxide–Al₂O₃, this study proposes heat transfer enhancement of thermal ice storage using novel hybrid nanofluids of aqueous graphene oxide–Al₂O₃. Thermal conductivity of aqueous graphene oxide–Al₂O₃ nanofluid was measured experimentally over a range of temperatures (0–70 °C) and concentrations. Thermal conductivity of ice mixing with the hybrid nanoparticles was tested. The influences of pH, dispersant, ultrasonic power and ultrasonic time on the stability of the hybrid nanofluids were examined. A new model for the effective thermal conductivity of the hybrid nanofluids considering the structure and Brownian motion was proposed. The results showed that pH, dispersant, ultrasonic power level and ultrasonication duration are important factors affecting the stability of the hybrid nanofluids tested. The optimum conditions for stability are pH = 11, 1% SDS, 375 W ultrasonic power level and 120 min ultrasonic application time. The thermal conductivity of hybrid nanofluids increases with the increase of temperature and mass fraction of nanoparticles. A newly proposed thermal conductivity model considering the nanofluid structure and Brownian motion can predict the thermal conductivity of hybrid nanofluids reasonably well.

Ensuring operational integrity in large-scale equipment hinges on effective fault prediction and health management. Prognostics and health management (PHM) face the challenge of accurately predicting remaining useful life (RUL) using multivariate sensor data. Traditional methods often require extensive prior knowledge for indicator construction and processing. Deep learning offers a promising alternative. This study presents a multi-channel multi-scale deep learning approach. Initially, an improved Savitzky‒Golay filter (ISG) addresses challenges posed by large and rapidly changing data volumes, enhancing data preprocessing. Subsequently, a framework integrates convolutional neural networks (CNNs) with long short-term memory (LSTM) to capture hierarchical signal information and make integrated predictions. The CNN extracts spatial features from multi-channel input data, while the LSTM captures temporal dependencies. By fusing outputs from both components, the framework enhances predictive accuracy and robustness for complex operational datasets. Experimental validation on the C-MAPSS dataset tests various fusion methods and CNN depths, determining parameters and evaluating filtering effectiveness. Comparative analyses show promising performance, particularly under dynamic conditions. While not optimal for predicting multiple fault types, it outperforms classical algorithms, especially in single fault type prediction tasks.

With environmental issues arising from the excessive use of fossil fuels, clean energy has gained widespread attention, particularly the application of lithium-ion batteries. Lithium-ion batteries are integrated into various industrial products, which necessitates higher safety requirements. Narrowband Internet of Things (NB-IoT) is an LPWA (Low Power Wide Area Network) technology that provides IoT devices with low-power, low-cost, long-endurance, and wide-coverage wireless connectivity. This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the data to local monitoring centers and cloud platforms via TCP and NB-IoT. Experimental validation demonstrates that the design functions effectively, accomplishing the monitoring and protection of lithium-ion battery packs in energy storage power stations.

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.

With the rise of digital twin technology, the application of digital twin technology to industrial automation provides a new direction for the digital transformation of the global smart manufacturing industry. In order to further improve production efficiency, as well as realize enterprise digital empowerment, this paper takes a welding robot arm as the research object and constructs a welding robot arm digital twin system. Using three-dimensional modeling technology and model rendering, the welding robot arm digital twin simulation environment was built. Parent–child hierarchy and particle effects were used to truly restore the movement characteristics of the robot arm and the welding effect, with the help of TCP communication and Bluetooth communication to realize data transmission between the virtual segment and the physical end. A variety of UI components were used to design the human–machine interaction interface of the digital twin system, ultimately realizing the data-driven digital twin system. Finally, according to the digital twin maturity model constructed by Prof. Tao Fei’s team, the system was scored using five dimensions and 19 evaluation factors. After testing the system, we found that the combination of digital twin technology and automation is feasible and achieves the expected results.

To prevent the frequent occurrence of transmission line galloping accidents, many scholars have carried out studies. However, there are still many difficulties that have not been solved. To address the issues that have arisen during the installation of the monitoring system, a new installation technique for the galloping monitoring terminal structure has been developed, and structural design and transmission line impact have been taken into account. A method combining Kalman and Mahony complementary filtering has been shown to solve the problem of wire twisting when galloping is taken into account. The displacement is derived by double-integrating the acceleration, although the trend term has a significant impact on the integration result. To handle the trend term issue and other error effects, a method combining the least-squares method, the adaptive smoothing method, and the time-frequency domain hybrid integration approach is used. Finally, the monitoring terminal’s structural design is simulated and evaluated, and the measured amplitude is assessed on a galloping standard test bench. The difference between the measured amplitude and the laboratory standard value is less than 10%, meeting the engineering design criteria. And the galloping trajectory is identical to the test bench trajectory, which is critical for user end monitoring.