Failure diagnosis chart clustering method based on network dividing

Abstract

The invention discloses a fault diagnosis spectral clustering operation method based on network partition. The method comprises the steps that: the fault diagnosis is molded into a network partition by the characteristic of network description fault sample which consists of nodes and relations; the objective function of the partition is made by utilizing the smallest and the largest criterion of the comprehensive evaluation of larger similarity between classes and smaller similarity inside a class; the objective function is optimally solved by a method of spectral clustering based on the theory of spectrogram; the operation method can acquire the state characteristics more quickly and acquire a comparatively high diagnosis accurate rate. The fault diagnosis embodiment of a UCI standard data set and a four-grade compressor proves the quick and effective performance of the operation method.

Description

technical field [0001] The invention belongs to the field of fault diagnosis, and relates to an application of a clustering method in the field of fault diagnosis—a fault diagnosis spectrum clustering method based on network segmentation. This method can be used to solve the problem of fault data feature extraction and fault identification and classification in fault diagnosis. Background technique [0002] Fault diagnosis is essentially a pattern recognition problem, which is to identify and distinguish normal and abnormal (fault) states according to the collected raw data and by analyzing the hidden state characteristics in the data. Divide data samples (observables) into separate classes, each sample class corresponding to a device state. Therefore, fault diagnosis actually has to solve the pattern classification / clustering problem. [0003] Fault diagnosis applications often face the following situations: i) obtain a large number of unlabeled data samples; ii) the natu...

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Problems solved by technology

However, due to its high computational complexity and the lack of guidance and feasible ideas for the practical application of the algorithm, there is no literature report on its application in the field of fault diagnosis.

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