Search

The rapid compression machine measures ignition delay time at high pressures and low to intermediate temperatures. However, unavoidable facility effects, such as compression and heat loss, shift the measurements away from ideal (adiabatic and constant volume) values to varying extents. Consequently, the ignition delay times measured by different facilities can be in large deviations, especially for fuel mixtures without negative temperature coefficient behavior. To address this issue, this work proposes a standardization algorithm that correlates the measurements to the ideal ignition delay times. The algorithm applies the Livengood–Wu integral method inversely and adopts a Bayesian approach to optimize the correlation parameters. The ignition delay times of an ethanol mixture under distinct facility effects were further used to test the performance of this algorithm. The results show that the dispersed ignition delay times can be effectively standardized within 20%, facilitating the direct comparison of measurements from different facilities. By setting a proper residual target of the algorithm, reasonable standardization accuracy can be achieved. This method enables a significantly easier interpretation of the rapid compression machine experimental data and can be broadly applied to any fuel mixtures exhibiting single-stage ignition characteristics. [ABSTRACT FROM AUTHOR]

Fault diagnosis plays a pivotal role in ensuring the reliability and efficiency of industrial machinery. While various machine/deep learning algorithms have been employed extensively for diagnosing faults in bearings and gears, the scarcity of data and the limited availability of labels have become a major bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep networks. To overcome the limitations of insufficient labeled data and domain shift problems, an intelligent, data-driven approach based on the Triplex Transfer Long Short-Term Memory (TTLSTM) network is presented, which leverages transfer learning and fine-tuning strategies. Our proposed methodology uses empirical mode decomposition (EMD) to extract pertinent features from raw vibrational signals and utilizes Pearson correlation coefficients (PCC) for feature selection. L2 regularization transfer learning is utilized to mitigate the overfitting problem and to improve the model's adaptability in diverse working conditions, especially in scenarios with limited labeled data. Compared with traditional transfer learning approaches, such as TCA, BDA, and JDA, which demonstrate accuracies in the range of 40–50%, our proposed model excels in identifying machinery faults with minimal labeled data by achieving 99.09% accuracy. Moreover, it performs significantly better than classical methods like SVM, RF, and CNN-based networks found in the literature, demonstrating the improved performance of our approach in fault diagnosis under varying working conditions and proving its applicability in real-world applications. [ABSTRACT FROM AUTHOR]

Urban rail transit encounters supply–demand contradictions during peak hours, seriously affecting passenger experience. Therefore, it is necessary to explore and optimize passenger-flow control strategies for urban rail transit stations during peak hours. However, current research mostly focuses on passenger-flow control at the network level, and there is insufficient exploration of specific operational strategies at the station level. At the same time, the microscopic simulation model for passenger-flow control at the station level faces the challenge of balancing efficiency and accuracy. This paper presents a simulation optimization approach to optimize the station-level passenger-flow controlling measures, based on a queueing network and surrogate model, aiming to improve throughput, minimize congestion, and enhance passenger experience. The first stage of the method modeled the urban railway station using queueing network theory and multi-agent theory, and then built a mesoscale simulation model that was based on an urban railway station. In the second stage, a passenger flow management and control model for ingress flow was established by combining the Kriging model with a queuing network model, and the particle swarm optimization algorithm was used to solve the model. On this basis, a simulation optimization method for station passenger-flow control was established. Finally, we conducted an example analysis of Zhongguancun Station on the Beijing subway. By comparing the simulation results before and after control, as well as comparing the optimal control scheme obtained by this method with the results of other control schemes, the results showed that the simulation optimization method proposed in this paper can propose an optimal passenger-flow control scheme. By using this method, stations can significantly enhance sustainability. For example, the method not only saves human resources but also effectively avoids or reduces congestion, boosting passenger travel efficiency and safety. By minimizing wait times, these methods lower energy consumption and support the sustainable development of public transportation systems, contributing to more sustainable urban environments. [ABSTRACT FROM AUTHOR]