Vector Spectrum-Support Vector Data Description And The Research Of Its Application In Fault Diagnosis

In the equipment fault diagnosis, the traditional methods of data processing can only analyze the data for the single channel, while the single-channel data often can not completely characterize out of the information on the equipment space motion. As one of the full-information analysis methods, the full vector spectrum analysis technology while dealing with homologous multi-channel fault signal, it can reflect a more comprehensive and accurate information about spatial characteristics of the rotor movement. Based on that, this paper will combine the full vector spectrum technology with support vector data description, and then propose the vector spectrum support vector data description (VSSVDD) fault diagnosis method. For the problem which support vector data description (SVDD) classification method is limited in the number of training samples, this paper presents the second modification of the SVDD classifier, Dynamic support vector data description (DSVDD) method. This method continue to update the boundary of classification by injecting new samples for the training samples and thus characterize out of the boundary regions of target samples. The problems which this paper studied and solved are as follows:First, the support vector data description method is based on statistical learning theory. The introduction of the kernel function can make the nonlinear problems in low-dimensional space transformed into linear problems in high dimensional space. Selecting different kernel functions makes the effect of SVDD classification different.Second, used full vector spectrum analysis method to analyze and process sampled data and extracted the amplitude on typical frequency multiplication as a feature vector of SVDD classifier. Experiments showed that the effect of SVDD classifier extracted by vector spectrum feature was more obvious than that without the extraction. Verified by an experiment, the vector spectrum Support Vector Data Description fault diagnosis method is feasibility and validity as to classifying the test samples.Third, used vector spectrum support vector data description method to introduce the concept.of the membership grade and the relative distance for degradation assessment of equipment performance to avoid the impact brought by the different boundaries of super-sphere of different, so that the state the test samples can be more represented more accurately; while reflects the process of the state changing.Fourth, proposed an improvement of SVDD, that is dynamic support vector data description. The raise of this method changed the current situation where after the classifier experiencing a training, classification boundaries never be changed in the original classification method of SVDD, It putted the goal test sample together with the support vector prior to this test to form a new set of training samples, and then re-trained SVDD. Thus training samples continuously updated classification boundary which also better reflect the characteristics of normal Status of the device.