Recent Patents on Mechanical Engineering - Volume 16, Issue 2, 2023

Background: Among the research on the influencing factors of environmental information disclosure, scholars rarely identify the factors from the perspective of the enterprise’s external environment, especially peer enterprise behavior. In fact, the disclosure of environmental information by most enterprises in China is still only voluntary, and the form of disclosure is so chaotic that it is easy to be influenced by other enterprises. Objective: This study aimed to determine whether a firm's EID is affected by peer firms and contribute to the existing literature on the influencing factors of EID. Methods: An analytical framework incorporating the herd behavior hypothesis, the legitimate theory, and the stakeholder theory is constructed, and fixed effect estimation, as well as a two-stage least square, is used to test the hypotheses. Results: There is a peer effect on environmental information disclosure of high-polluting firms. It has been observed that the focal firm imitates the disclosure behavior of small peer firms more than the large peer firms. Moreover, a peer effect of environmental disclosure on sensitive and non-sensitive information is also reported, but the peer effect on sensitive information is larger than that on nonsensitive information. Conclusion: First, policymakers need to realize that there is a peer effect involved in EID among highpolluting firms and improve the binding force of environmental regulations. Second, there are “demonstration effects” involved in EID. In the practice of regulations on disclosure, the smaller firms need to be under stricter scrutiny and set as models of EID to improve the efficiency of supervision and regulation. Third, enterprises have a stronger peer effect on the disclosure of sensitive information. Governments should strengthen the supervision of sensitive information disclosure.

Aims: To evaluate the environmental benefits of the manufacturing system and optimize the process parameters, a more effective method of evaluating environmental benefits should be adopted, which can be used for process parameters optimization. Background: Mechanical manufacturing systems generally include raw materials, energy, products, waste emissions, capital, labor, and other elements. The Extended Carbon-Emissions Accounting (ECEA) method is an extension of the traditional Carbon Emissions Accounting (CEA) method, which can account for capital and labor elements in equivalent carbon emissions similar to other elements. Methods: Through the ECEA method, the Extended Carbon Dioxide Emissions (Ex-CDE) of the manufacturing system are obtained by accumulating the value by multiplying the consumption of each element and its Extended Carbon Emission Factor (Ex-CEF), which can be used as the environmental benefits of the manufacturing system. Then, a comprehensive optimization case of lowcarbon, high-efficiency, and low-cost can be built, including all consumption elements, and the COMPLEX algorithm is used to solve this optimization problem. Results: Taking a turning process as an example, through the ECEA method combined with the COMPLEX algorithm, the process parameters for environmental benefits or comprehensive optimization can be solved. Conclusion: ECEA method is suitable for environmental benefits analysis and optimization of the manufacturing system.

Background: The wheel out-of-round fault of urban rail vehicles has a very important impact on the safe operation of urban rail trains. Therefore, it is of great significance to achieve an accurate diagnosis of the wheel out-of-round fault of trains. Objective: The purpose of this paper is to summarize the diagnosis methods of the wheel out-of-round fault, and propose a new diagnosis method based on vibration data images, which can effectively identify the wheel out-of-round fault. Methods: The one-dimensional vibration signal is converted into a two-dimensional texture matrix. The Statistical Geometrical Features (SGF) method extracts the feature information of the twodimensional gray image and combines it with a support vector machine for training and recognition to achieve the fault diagnosis of the wheel out-of-roundness. Results: The feasibility and accuracy of the method are verified by simulation and experimental signal analysis, respectively. The experimental results show that the overall recognition accuracy of the model simulation data and the two-wheel experimental bench data exceeds 91%, exhibiting significantly high fault identification accuracy. Conclusion: In this paper, a wheel out-of-round fault diagnosis model based on vibration data images has been established by analyzing the vertical dynamic signal of the axle box, which has the advantages of fast recognition in combination with two-dimensional grey-scale images, no signal preprocessing, and high recognition accuracy. It provides a new method for monitoring and diagnosing wheel out-of-round faults in urban rail vehicles.

Background: Lithium-ion batteries are widely used in new energy vehicles and energy storage systems due to their superior performance. However, lithium batteries are prone to safety problems in the use process, so the fault diagnosis technology of lithium batteries has attracted more attention. Objective: This study aimed to ensure the safety of lithium batteries and accurately and timely diagnose the soft short circuit (soft SC) fault of lithium battery. Methods: Aiming at the energy storage lithium battery pack, this study proposed a soft short-circuit fault diagnosis method for the lithium-ion battery pack based on the improved Extended Kalman Filter (EKF) algorithm. First, the 1^st-order RC equivalent circuit model of normal battery and soft SC fault battery was established, and model parameters were identified using Recursive Least Squares with Forgetting Factor (FFRLS). Then, using the improved EKF, the state of charge (SOC) of a single cell was estimated, and the difference between the calculated SOC and the estimated SOC by the coulomb counting method was used to detect soft SC faults and compared them with the reference data. Finally, the SC resistance value indicated the severity of the fault. Results: The proposed method could accurately diagnose the soft short circuit fault, and the error was found to be lower than the traditional EKF algorithm. The estimation error was about 0.4% for the battery with slight failure and about 1.5% for the battery with serious failure. Conclusion: The experimental results showed that the improved EKF algorithm could estimate the SOC difference more accurately, and the effect of soft SC fault diagnosis was better. At the same time, it could quantitatively identify the size of the short circuit resistance, which is very helpful for the subsequent management of the battery system.

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effectiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high.