Research On Vibration Data Pattern Matching Based On Data Mining

Discovering the characteristic of vibration data are beneficial to identify the target and statein monitor environment, which have great academic value and practical significance in the areaof security defense. With careful study and comparison of several classification methods, thispaper explores a new method to identify pattern of vibration data. The model of vibrationamplitude and distance is built, and the location of the vibration source is judged by the model.At the same time, a prototype software system was developed. The main contents are as follows:According to the problem of the low pattern recognition rate of the vibration source, ak-Nearest Neighbors method based on projection scale (KNNPS) is proposed. The methodintroduces the concept of projection scale. First, the independent component analysis (ICA) isused to separate noise from the signal. Then the discretization of continuous attributes is used todescend dimension. Finally, the similarity is measured by projection scale, and the pattern ofvibration signal is matched. The accuracy rate, error rate, detection rate and pattern matching rateare used to evaluate the performance of KNNPS, KNN, and NB algorithms. The experimentalresults show that the KNNPS algorithm outperforms the KNN and NB algorithm. Throughanalyzing the results of the classification，the number of the vibration source can got by thecategory of classification, and the distance of vibration source can got by strength of the vibrationamplitude of the same category.The location of the vibration source cannot be correctly estimated by strong and weak of thevibration amplitude, so the amplitude of fade model is built to obtain the distance of vibrationsource. The distance of vibration source is estimated effectively by single sensor, and thecoordinate of vibration is located by multi-sensor.Based on these researches above, the independent component analysis method (ICA) andclassification method are applied in the vibration data pattern matching system. The system consists of the signal separation module, the vibration source judgment module, and signalmatching module. The experimental results show that this system works well and could reachesthe expected target.