Evolutionary computation-based method for diagnosing compressor fault

Abstract

The invention discloses an evolutionary computation-based method for diagnosing compressor fault. The method comprises the following steps: data about the working condition of a compressor is collected and acquired; an observation point is taken as a node, and the correlation between observation points produced by the actual compressor physics; a weighting undirected graph G(V,A) is set up for measured data; the distance between fault data points is gauged by means of a relational graph instead of the traditional Euclidean distance method; the data about the fault of the compressor is converted into a problem with the partitioning of the weighting undirected graph G; the Pareto Principle structural map is adopted for partitioning cost function; an evolutionary calculation chart partitioning computation based on the improvement on the complexity system theory is designed to partitioning the relational graph; and the clustering/classification of the compressor fault data is realized through the optimal computation of the cost function, so that the purpose of fault diagnosis can be achieved.

Description

technical field [0001] The invention belongs to fault diagnosis methods, in particular to a method for diagnosing compressor faults based on improved evolution calculation. This method can be used to solve the problems of fault data feature extraction, fault identification and classification in compressor fault diagnosis. Background technique [0002] Due to the increasing complexity of the compressor system, the number of detected state parameters increases, which increases the difficulty of processing fault detection data. Its main manifestations are: the complexity of the distribution space of fault data state parameters and the redundancy of data samples. In previous fault detection, Euclidean distance was generally used to measure the distance between data and determine the clustering / classification of data, but Euclidean distance can only correctly reflect the relationship between data in spherical space. If the system fails Data distribution such as figure 1 As sho...

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Problems solved by technology

The purpose of the present invention is to express the relationship between fault data points in the form of a relational diagram, establish a set of new data relationship metrics to replace the Euclidean distance, and overcome the fact that the Euclidean distance can only accurately express the data relationship in a spherical space Weaknesses; combine complex system theory and evolutionary computing technology to solve the technical difficulties of multi-noise data graph segmentation, realize clustering / classification of fault data, and then extract rules to complete fault diagnosis of compressor system

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