Support vector machines (SVMs) are used for condition trend prediction of mechanical equipment. The influence of cost functions, kernel functions and parameters on prediction performance of SVMs is studied. Cost functions play a very important role on prediction performance of SVMs. Experiments show that the prediction performance of ε insensitive cost function is superior to that of least square cost function. At the same time, analysis of the experimental results shows that four kernel functions have very close prediction performance in short term prediction, and radial basis function kernel has better prediction performance than other kernels in long term prediction. In comparison with traditional Back Propagation (BP) neural network, Radial Basis Function (RBF) network and Generalized Regression Neural Network (GRNN), experiments show that SVMs, which implement the structure risk minimization principle, obtain the best prediction performance.

为了从故障诊断实例的数据资源中知识获取，对具有连续属性值的故障实例数据表转化为Roughset（RS）理论离散数据类型的决策表的正确映射进行了研究。将改进的k-means聚类算法用于故障实例数据表的离散映射方案设计。在设置故障实例的导师决策类别数为聚类数k对论域划分的基础上，提出了根据均值聚类中心排序序号构造离散映射符号集、相对均值聚类中心由相似测度确定连续属性值映射编码的离散化方案。实例表明，该方法反映了转子振动故障特征的一般规律，断点设置具有动态自适应和抗干扰特性。获得的决策规则可用于构造和扩充故障诊断知识库。

以某种四缸内燃机为物理样机原型，在高性能工作站上建立了模拟西部环境的对应虚拟样机试验系统，模拟了低气压、沙尘等典型西部环境因素对内燃机摩擦学、动力学行为的影响，研究中使用自编程序和专业软件相结合的办法，获得了该数字系统模型较为完整的虚拟试验数据及其结构对性能影响的数据，为内燃机在西部环境下更好的运行和维护提供了理论上和技术上的支持。

介绍了用发明问题解决理论（TRIZ）中冲突的概念可得到产品创新的高层次解，在此基础上，进一步阐述了设计问题的五种结构，并对典型的点状问题（物理冲突）从时间、空间域将其划分为三种形式，建立了物理冲突的表达模型，对与之相对应的启发式原理进行了拓展。据此理论，建立了产品概念设计过程模型，并编程实现这一过程。用工程实例说明了应用。

Support vector machine (SVM), developed by Vapnik et al, is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ability. A new type SVM named projected SVM (PSVM), which is a combination of feature vector selection (FVS) method and linear SVM (LSVM), is proposed in present paper. In PSVM, the FVS method is first used to select a relevant subset (feature vectors, FVs) from the training data, and then both the training data and the test data are projected into the subspace constructed by FVs, and finally linear SVM (LSVM) is applied to classify the projected data. The time required by PSVM to calculate the class of new samples is proportional to the count of FVs. In most cases, the count of FVs is smaller than that of support vectors (SVs), and therefore PSVM is faster than SVM in running. Compared with other speeding-up techniques of SVM, PSVM is proved to possess not only speeding-up ability but also de-noising ability for high-noised data, and is found to be of potential use in mechanical fault pattern recognition.