Mean influence value data transformation-based k-nearest neighbor fault diagnosis method

Abstract

The invention discloses a mean influence value data transformation-based k-nearest neighbor fault diagnosis method. The method includes the following steps that: S1, a data set X is collected; S2, standardization processing is performed on the data set X; S3, a BP (Back Propagation) neural network is constructed; S4, the mean influence value (MIV) of the data set is calculated; S5, a weighted dataset X' is calculated; and S6, the weighted data set X' is inputted into a k-nearest neighbor classifier for fault diagnosis, so that a fault result is obtained. According to the mean influence valuedata transformation-based k-nearest neighbor fault diagnosis method of the present invention, the standardized data are processed by the BP neural network, so that the mean influence value (MIV) of data change can be obtained; the MIV can reflect the change condition of the weight matrix of the BP neural network and is the best index for evaluating the correlation of the input parameters of the BPneural network, and therefore, the MIV can determine the weight of the influence of the input neurons of the neural network on the output neurons; and the inputted data set is processed according tothe MIV, so that effective features in the data set can be highlighted, and therefore, the dimensions of the data can be reduced, and correlation between the inputted data set and the output can be enhanced.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis, and in particular relates to a k-nearest neighbor fault diagnosis method based on average influence value data transformation. Background technique [0002] In the current industrial production and social services, all kinds of automation equipment are becoming more and more complex, making fine modeling more difficult. However, with the development of sensor technology in recent years, we can obtain a large amount of monitoring data. There are often a large number of highly correlated state variables, and the instantaneous sampling values ​​of these variables reflect key information such as whether the equipment is running normally and whether the system output is up to standard. Therefore, the fault diagnosis method based on data drive has been paid more and more attention by people. However, the data-driven method must face the problem of different dimensions of various variables. Th...

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Problems solved by technology

For example, a modular principal component analysis algorithm based on intra-class weighted average is mainly aimed at the problem of low recognition rate of principal component analysis algorithm in face recognition

There is also a relative principal component analysis (Relative Principle Component Analysis, RPCA) method, which uses the prior information of the system to introduce the weight of each variable to eliminate the problem of difficult feature extraction due to the "uniform" distribution of standardized data. But the disadvantage of this method is that it requires a lot of prior information from the system, which is difficult to obtain in practical engineering applications

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