This study, rooted in extension theory and the principles of knowledge engineering, explores and formulates a novel method for generating sports protective gear designs. Given the critical role of sports protective gear in safeguarding athletes from injuries, coupled with escalating demands for product quality, the aim is to uncover a more effective approach to innovative design. This method involves formalizing modeling of various elements in the design process and representing this information in the elemental form of knowledge engineering. Through the related analysis, divergent analysis, as well as permutation and conduction transformations of these elements, innovative design schemes for sports protective gear are generated. This process not only optimizes design schemes in depth but also ventures into new design methods and processes. The objective is to offer a novel perspective in integrating extension theory and knowledge engineering in the design of sports protective gear, aspiring to provide more effective strategies to enhance existing design workflows. The goal of this new design method is to produce sports protective gear that is both practical and innovative, thereby enhancing the safety and enjoyment of athletes.

目前国内外对齿轮以及轴承的故障动力学研究已经很完善,但两者复合的故障动力学研究还不够深入.将面齿轮副与滚动轴承的动力学耦合到一起,建立齿轮-轴承耦合动力学模型,通过实验研究获得了模型在单一以及复合故障下的时域以及频域响应规律,为齿轮传动故障诊断提供理论依据.

轴承的健康状态对于雷达驱动结构以及直升机传动机构等旋转机械的正常运作至关重要,针对滚动轴承工况复杂,存在噪声,振动信号各故障标签样本不足且不平衡的特点,基于扰动训练样本的可变形卷积和深度残差块结构,提出了一种改进一维卷积神经网络的滚动轴承故障诊断方法.通过设置可变形卷积提高对故障局部特征提取的能力,引入改进的深度残差块来提高模型的泛化能力和对训练数据的敏感性,在加入训练数据时,通过设置训练扰动层加入扰动样本,提升模型的鲁棒性.以凯斯西储大学轴承数据集为实验数据集,分割训练集和测试集,实验结果证明了所提方法的有效性,TD-DCCNN算法在信噪比为0 的情况下仍可以达到90.35%的平均准确率,与其他诊断算法相比有一定的优越性.

The quest for heightened precision in fuzzy system predictions has culminated in the development of an innovative model that integrates a Fuzzy K-Clustering (FKC) algorithm with a fuzzy neural network (FNN). In this approach, the novel FKC algorithm, herein introduced, undertakes the clustering of sample data. Subsequently, the clustering outcomes inform the configuration of the FNN, specifically guiding the determination of node quantities across its layers and the initial network parameters. A distinctive hybrid learning algorithm, designated as the Conjugate Recursive Least Squares (CRLS), facilitates the optimization of network parameters via distinct methods tailored to parameter types. This model underwent empirical validation using 2-minute interval average wind speed data from surface meteorological stations in China. Analytical comparisons between model predictions and actual wind speed data revealed an average absolute error of 0.2764m/s, an average absolute percentage error of 2.33%, and a maximum error of 0.6035m/s. The findings substantiate the model's superior predictive capability. This study thus presents a significant advancement in fuzzy system prediction methodologies, underscoring the potential of the FKC and FNN in complex data analysis.

滚动轴承作为旋转机械中的关键部件,对其剩余使用寿命RUL(remained useful life)的准确预测可以帮助维修人员及时制定维修计划,延长设备工作时间,保证安全.由于利用数学建模精确建立轴承退化过程的模型涉及到复杂的物理过程,所以以深度学习为基础的基于数据驱动的方法已经成为主流方法.提出了一种融合混合膨胀卷积与自适应斜率软阈值函数的时间卷积神经网络TCN-HS(temporal convolutional network with hybrid dilated convolution and self-adaptive slope thresholding)用于滚动轴承寿命预测.模型使用混合膨胀卷积HDC(hybrid dilated convolution)解决了栅格效应问题,并利用自适应斜率软阈值函数(self-adaptive slope thresholding)进一步筛选特征.为了验证TCN-HS模型的有效性,基于PHM2012 轴承数据集进行了实验,结果表明:改进方法提升了模型的性能,预测结果准确.

This study explores the extension configuration methods of complex product conceptual design, seeking to improve the product design efficiency and design quality. The paper firstly reviews the literature on element representation models of multi-type design knowledge, followed by a review on extension design models for the rapid configuration of complex product conceptual design. The extension transformation method for the rapid configuration design of complex product conceptual design is also reviewed. With the analysis of the extension reasoning model for the rapid configuration design of complex product conceptual design, the research proposes a new model of extension reasoning for the rapid configuration design of complex product conceptual design. This model of extension design would enhance the rapid configuration design and conceptual design of large and complex products. Detailed steps of the algorithm implementation are also presented. This study also tests the validity and operability of the model and the algorithm with the design case of a large hydro-turbine product design.

In bearing fault diagnosis, due to the insufficient obtained supervised data and the inevitable noise contained in the vibration signals, the problem of clustering bearing fault diagnosis with imbalanced data containing noise is caused. Thanks to the ability to quickly and fully learn boundary information in small samples, the extension neural network-type 2 algorithm (ENN-2) has the potential in imbalanced data clustering and has been gradually applied in fault diagnosis. Therefore, in order to improve the unstable clustering performance of ENN-2 caused by its heavy dependence on input order of samples, a novel algorithm called linked extension neural network (LENN) is developed by redesigning the correlation function and its iterative method, which greatly reduces the clustering iteration epochs of the algorithm. In addition, an evaluation index of clustering quality for this novel algorithm, extension density, is also proposed. After that, a bearing fault diagnosis model of variational mode decomposition (VMD) based denoising and LENN is proposed. Firstly, VMD is used to get intrinsic mode functions (IMFs), and the correlation coefficients of IMFs are calculated for signal denoising. Secondly, the features are extracted from denoised signals and selected by PCA algorithm, and the fault diagnosis is finally completed by LENN. Compared with ENN-2, K-means, FCM, and DBSCAN based models, the proposed model identifies the faults with different severities more accurately and achieves superior diagnostic ability on different imbalance degrees of datasets, which can further lay a foundation for clustering fault diagnosis based on vibration signals.

Due to the configuration process of a complex product scheme, a design structure often has the characteristics of multi-level, multi-attribute, creativity, and complexity; in order to improve the efficiency and quality of product scheme design, it has important research value to reasonably organize, reason, and reuse design knowledge. In this paper, the extension modeling problem under the extension design mode of complex product scheme is studied, the multitype design knowledge element modeling expression model of complex product scheme design is given, and the extension process model and the implication process model of requirement analysis of complex product scheme design is established. A new demand element weight assignment method based on extension distance is proposed to obtain accurate demand analysis index weight from the perspective of combined qualitative and quantitative analysis. On the basis of constructing the extension correlation degree of demand primitives, this paper puts forward the implementation method of the extension design pattern for the demand analysis of a complex product scheme design and gives the specific implementation algorithm. Finally, an example of product design is given to illustrate the method, and the results show the effectiveness and operability of the method.

为了对无人驾驶汽车内饰设计复杂问题进行明确量化、评价和提高设计效率,将可拓学应用于无人驾驶汽车内饰设计.提出面向无人驾驶汽车内饰设计问题的界定方法、共轭分析、方案生成算法以及构建了无人驾驶汽车内饰设计评价指标体系.以基于2030年90后父母、孩子和宠物狗一起去近郊旅游的无人驾驶汽车内饰设计为例,通过基于可拓理论的无人驾驶汽车内饰设计方案生成算法,获得3个有效的设计创意.并进行设计表达,使其具体化为设计方案,最后进行优度评价获得最优方案.设计结果验证了该方法的可行性和有效性.

蕴含关系是影响复杂产品方案快速配置设计的重要因素.为了有效提升复杂产品方案可拓配置设计的能力,针对复杂产品方案设计过程中蕴含信息的有效表达、挖掘、推理和重用等进行了研究.对大型复杂产品方案可拓设计过程中的可拓本体概念模型、可拓本体蕴含系的信息量计算模型、基于可拓本体蕴含系的设计蕴含关系挖掘模型、基于可拓本体蕴含系的可拓重用度计算模型等进行了分析,提出了一种基于可拓本体蕴含系的复杂产品可拓设计模式,并给出了相应模型与算法的实现步骤和框架.通过具体的设计实例对文中的模型和算法进行了说明和验证分析,结果验证了模型和算法的有效性和可行性,从而为复杂产品方案可拓设计的顺利实施提供理论和工程应用支持.

In the process of optimization design of complex systems, there is often more than one optimization objective, and the multiple objectives to be optimized often conflict with each other, for the traditional mechanical optimization design methods to solve the problem of slow speed, poor optimization effect, etc., with the help of particle swarm algorithm proposed a Multi-objective particle swarm optimization algorithm based on object space decomposition(OSD-MOPSO). Firstly, a set of direction vectors is used to divide the object space into a number of uniformly distributed subintervals; then, the distribution density of solutions within the external archives is controlled by using the extension distance, so that the solutions in the whole object space are uniformly distributed; secondly, multiple solution evaluation and decision making are carried out with the help of the superior and inferior solution distances to quantity the degree of superiority and inferiority among solutions; finally, the multi-objective optimization test function is compared and tested, and the results are analyzed Finally, the effectiveness and stability of OSD-MOPSO are verified, and OSD-MOPSO is applied to a multi-objective optimization design example of an automotive transmission gear set to verily the optimization effect of OSD-MOPSO in actual engineering.

复杂机械产品运行状态预测分析往往准确率较低、推理时间较长、难于推理,难以获得有效结果.为此,给出了基于改进可拓神经网络的复杂机械产品运行状态预测分析模型.提出了改进的可拓距,基于该可拓距构建可拓神经元,建立复杂机械产品运行状态分析经典域模型,并对运行数据进行训练,形成复杂机械产品运行状态可拓神经网络预测分析模型.通过具体案例对算法和模型进行验证,并对比BP神经网络,结果表明了模型与算法的有效性和可行性.

在复杂机械产品设计过程中,针对知识模型存在的语义阐述完整性和可拓展性问题,对面向智能化设计的复杂机械产品知识建模进行了研究,提出了一种改进的复杂机械产品可拓本体模型.该模型在经典本体模型的基础上,融合可拓理论,对可拓本体概念、可拓本体相关定义以及可拓本体定理进行了拓展,给出了可拓本体模型建立的相关原则,提出了复杂机械产品可拓本体模型建立的具体实施步骤;最后,以某型号直升机为应用实例,对复杂机械产品可拓本体模型建立过程进行了验证分析.研究结果表明:所提出的可拓本体模型具有较好的完备性和可拓展性,能够对复杂机械产品智能化设计的顺利实施提供支撑作用.

A extension knowledge push model of automobile engine design was studied, aiming Aiming at at the problem that designers have a large demand and different requirements for design knowledge in the process of automobile engine collaborative design，the extension knowledge push model of automobile engine design is studied. Firstly，the extension push architecture of automobile engine design knowledge iwas established，and the design flow framework of the vehicle engine design extension knowledge push model iwas given. Then，,the extension element model of automobile engine design knowledge iwas established and fuzzified. Based on this，the construction process of the automobile engine design extension knowledge base iwas put forward. Finally，, the automobile engine knowledge push extension correlation function model was constructed based on the extension knowledge base，the automobile engine knowledge push extension correlation function model is constructed，,and then the extension knowledge push model for automobile engine design iwas established. The collaborative design results of collaborative design of thean automobile engine shows that the extension knowledge push model proposed for the automobile engine design is effective and feasible.

为了有效提升大型复杂产品方案设计结构配置的设计效率和质量,对大型复杂产品方案可拓集成设计进行了研究.对大型复杂产品方案可拓设计过程中的可拓本体概念模型、可拓本体库构建框架、方案设计可拓需求模式、方案设计可拓配置模式和方案设计可拓再设计模式进行了分析,提出了一种基于可拓本体概念的复杂产品快速设计可拓模式框架;通过将应用层、应用工具层以及环境支撑软件层相融合,建立了基于可拓设计模式的型号飞机产品快速设计可拓集成平台体系结构.研究结果表明:该可拓集成设计平台体系结构的建立,能够实现多层次、多属性、创造性、复杂性等特点的复杂产品方案设计的结构可拓配置,从而为复杂产品快速设计的顺利实施提供理论和工程应用支持.

针对复杂产品方案设计中的关联约束可拓推理问题,本研究提出了一种基于可拓本体相关网的复杂产品方案设计可拓推理模型.首先,建立了复杂产品方案设计可拓本体模型,并进行了可拓本体相关性分析;其次,通过领域知识获得产品方案设计方向,建立可拓本体特征的可拓关联函数,并基于信息模型建立可拓本体相关网;然后,给出了基于可拓本体相关网的方案设计可拓推理算法,通过进行设计方向多层级灰关联聚类分析和基于可拓本体相关网的关联度计算分析,进而获得满足设计需求的初始产品设计方案.最后,通过水轮机设计实例对文中的模型和算法进行了说明和验证分析,结果表明了模型和算法的有效性和可行性.

In data-driven fault diagnosis for turbo-generator sets, the fault samples are usually expensive to obtain, and inevitably with noise, which will both lead to an unsatisfying identification performance of diagnosis models. To address these issues, this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network (W-ENN). W-ENN is a novel neural network which has three types of connection weights and an improved cor-relation function. The performance of the proposed model is validated against Extension Neural Network (ENN), Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Extreme Learning Machine (ELM) based models. The results indicate that, on noisy small sample sets, the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability. The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets.

For the navigation problem of differential AGV, its motion model was established, and the inertial guidance method based on multi-sensor was adopted. The encoder, gyro, acceleration sensor, ultrasonic sensor and infrared sensor were selected to establish the Kalman filter multi-sensor. And the models and algorithms of the data fusion navigation and the obstacle avoidance were proposed. Further, the simulation calculation was conducted. The research results show that the navigation accuracy of AGV and navigation performance were improved by the method discussed in the paper.

The lightweight design of autobody involves multiple objectives and requires the collaboration between various disciplines.To improve the existing autobody lightweight designs, it is necessary to establish an accurate and objective evaluation method for lightweight effect.This paper proposes an extension decision tree (EDT) algorithm for lightweight design of autobody.The algorithm solves the contradictions in the autobody lightweight design were solved through divergence and convergence.The workflow of the solving process is shaped like a scalable diamond.Specifically, the knowledge of autobody structure was described accurately by extension modeling and extension divergence reasoning.Then, the lightweight design of autobody structure and material was achieved through extension transforms.Next, the EDT algorithm was constructed based on extension theory and the DT algorithm, and used to evaluate the lightweight effect of autobody.Finally, the effectiveness of the proposed algorithm as verified through a case study and a computer-aided engineering (CAE) simulation.The results show that our algorithm can accurately predict the weight reduction effect of autobody based on the case data, and generate a set of intelligent strategies to optimize the current design.The research results shed new light on intelligent evaluation of autobody lightweight design with multiple objectives and constraints.