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.

The vibration behaviour of a rotor system supported by hydrodynamic bearing is significantly influenced by the dynamic characteristics of the bearings. An identification procedure for stimating bearings oil-film coefficients is developed in this work. Eight linearized oil-film coefficients are identified by fitting the measured complex functions of frequency response to those calculated theoretically. The theoretical model and reliability are examined by computer simulation. From error analysis, some interesting conclusions are reached and a new identification method is suggested in order to reduce the influence of phase measurement errors on estimated results.

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。

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。

The experimental modal analysis (EMA) has been used to estimate modal parameters in many mechanical engineering problems, but it is mostly employed for nonrotating strucrures. A suocessflu application of an improved ITD Method in rotating machinery with oil-film bearings is described.