Reciprocating compressor intelligent diagnosis method based on EMD-PCA

Abstract

The invention relates to a reciprocating compressor intelligent diagnosis method based on EMD-PCA. Intelligent diagnosis of faults of a reciprocating compressor is realized by comprehensively using an empirical mode decomposition (EMD) method, a principal component analysis (PCA) method and a multi-class classification support vector method; the method is characterized in that common faults of the reciprocating compressor are subjected to feature extraction, the fault alarm and diagnosis are performed according to different sensitive features of different faults, acceleration signals of a reciprocating compressor cylinder are subjected to feature extraction by using empirical mode decomposition, and each group of acceleration signals have 30 features; then dimension reduction is performed by using principal component analysis to obtain a three-dimensional feature space; finally, five working conditions obtained by experiments are diagnosed by using multi-class classification support vector, and the intelligent diagnosis process framework of the reciprocating compressor is summarized and proposed. Through the adoption of the method, fault diagnosis accuracy under the five working conditions is high.

Description

technical field [0001] The invention relates to an intelligent diagnosis method of a reciprocating compressor, which comprehensively uses an EMD method, a PCA method and an SVM method to realize the intelligent diagnosis of a reciprocating compressor fault. Background technique [0002] The reciprocating compressor is the key equipment in many process industries. Its structure is complex and there are many sources of vibration excitation. Once a failure occurs, it will easily bring huge losses to the enterprise. Therefore, it is very necessary to study on-line monitoring and intelligent fault diagnosis of reciprocating compressors. Condition monitoring of reciprocating compressors is generally realized by monitoring physical quantities such as cylinder vibration, valve cover temperature, and dynamic pressure. Monitoring and analyzing the cylinder acceleration signal of the reciprocating compressor is an important means of fault diagnosis of the reciprocating compressor. Thr...

AI Technical Summary

Problems solved by technology

When identifying high-dimensional features, there is a phenomenon of dimensionality disaster. This phenomenon will inevitably lead to inaccurate fault feature identification. Therefore, on the premise of ensuring that the main information of the features is not lost, it is necessary to reduce the dimensionality of the features, and then perform feature analysis. identify

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