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The Euclidean distance discriminant function is simple in form, simple in physical concept, and convenient in calculation. One of its main drawbacks is that the importance of the various elements of the pattern vector is not considered. In the above identification, since the fault state is relatively obvious, the recognition false positive rate is not high. In some inconspicuous faults, the false positive rate is high, and its application range is not large.
The information distance judgment function information distance function constructed by the AR model residual variance is derived from the calculation of the information amount in the information theory. The KullbackLeibler information distance is applied in this paper. The specific method is as follows: According to the principle of information theory, the Kullback-Leibler information distance function expression sequence belongs to the KL information distance, the reference sample judgment result is to be detected D=D1-D2 judgment result to be detected sequence D=D1-D2 Judgment result 1-00177084 normal 1100722617 fault 2-00444127 normal 1200520454 fault 3-00499741 normal 1300709135 fault 4-00375393 normal 1400441531 fault 500008011 fault 1500438739 fault 6-00340185 normal 1600348067 fault 7-00204710 normal 1700601307 fault 8-00474051 normal 1800376673 fault 9- 00453434 normal 1900188865 fault 10-00444111 normal 2000319668 fault note: 5 sample misjudgment sequence. In this paper, the gear is in the normal state and the fault state as the reference sequence, and the above data is substituted into the information distance between the calculation and the reference sequence. D1=D2KL(RT, RR1) is the distance between the sample to be tested and the normal state sample, D2=D2KL(RT, RR2) is the distance between the sample to be tested and the fault state sample, and the sample to be tested belongs to the sequence with the smallest information distance. Finally, the judgment result is as shown.
It can be seen from the above results that the false positive rate of the method is relatively low, the recognition ability is strong, and the calculation is relatively simple. The failure of the general mold is an effective identification method.
Bayes classifier design The two distance discriminant functions discussed above are simpler to calculate, and the physical concept is also very obvious. It is very practical. However, it also has its shortcomings: First, the probability of occurrence of each population is not considered in the discriminant function; second, the loss caused by the wrong judgment is not considered. Bayes classifier is a kind of discriminant identification method proposed to solve these two problems. The criterion is more than the distance criterion, and the discriminative ability is stronger, but it requires the relevant information to be obtained in advance.
It can be seen from the derivation calculation that the false positive rate of the Bayes classifier is related to the assumed prior probability P(1)=P(2)=05, and the other is related to the standard mode. Standard patterns are often obtained from training samples. The typicality of training samples and the number of training samples will directly affect the false positive rate of Bayes classifiers. In practice, typical training samples should be used to construct the discriminant function as much as possible to minimize the false positive rate and reduce its loss.
Conclusion In this paper, the statistical pattern recognition method in the pattern recognition method is used to diagnose and identify the fault of the gear, and the fault state is quantitatively judged, and good results are obtained, which overcomes the qualitative judgment in the fault diagnosis. The identification error comes, so it has good application value. The above three identification methods are combined, each having its own characteristics and applicability. In contrast, Bayes classifier is a better classification method, which considers the probability of occurrence of each population and the loss caused by misjudgment, which greatly improves its recognition accuracy. In this paper, because of the state categories involved. Less, the comparison of recognition accuracy is not very obvious, but for complex fault diagnosis, it will show greater superiority, especially in practical applications, it has a good application prospect.
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