提出了一种基于多传感器信息融合技术对油品质量进行在线监测的新方法, 通过对粘度、温度、介电常数的测量, 建立了神经网络油品质量状态监测模型, 并给出了系统的设计方案。

复杂、并发故障诊断的难点在于这些故障的振动信号很复杂，特征很难获取。文中阐述了从实例数据库中挖掘故障特征的整体结构，定义了信号的绝对、相对和梯度特征及相应的绝对、相对和梯度模式实例，进而阐述了应用模糊聚类分析挖掘特征模式的方法。最后以往复式压缩机实例挖掘系统为例说明了该原理的应用。

产品状态空间由功能、行为、结构3部分组成, 3个空间之间的映射结构形成设计过程的演化模型。文中集中研究了功能行为关系，利用模糊评述建立了功能与行为的映射模型；而功能与行为之间的复杂耦合关系一直是分析的难点，对此，引入正交模型以解藕功能与行为关系，并构建公共组合行为变量，从中提取出行为特征，揭示功能与行为映射结构。最后，依据文中的数学模型以及分析方法，针对某机电产品—-多功能综合实验台，分析产品功能与行为映射结构。

在回顾传统的设计方法及状态监测的传统任务的基础上，展望现代设计技术与状态监测诊断内涵及共相互联系，提出状态监测的研究方向。

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.