Reciprocating compressor fault diagnosis method based on improved RCMDE

Abstract

The invention belongs to the technical field of mechanical fault diagnosis, and particularly relates to a reciprocating compressor fault diagnosis method based on improved RCMDE, which comprises the following steps that machine body surface time domain vibration signals of a reciprocating compressor under different operation conditions are acquired, and an initial vibration signal is processed by adopting parameter optimization variational mode decomposition to obtain an intrinsic mode function component; according to a kurtosis-correlation coefficient criterion, a group of components containing the most abundant information amount is selected, and a fault signal is reconstructed; the nonlinear behavior of the vibration signal after noise reduction is quantitatively analyzed through the improved fine composite multi-scale dispersion entropy, and a fault feature vector is formed; input features are selected in a dimensionality reduction mode through a kernel principal component analysis method and input into a kernel extreme learning machine to be classified and recognized, and the operation state and the fault type of the reciprocating compressor can be distinguished. Through improved fine composite multi-scale dispersion entropy analysis, nonlinear behaviors of vibration signals are quantitatively described, feature vectors are formed, and fault types can be diagnosed more accurately.

Description

Technical field: [0001] The invention belongs to the technical field of mechanical fault diagnosis, in particular to an improved RCMDE-based reciprocating compressor fault diagnosis method. Background technique: [0002] Reciprocating compressors are widely used in petrochemical and other fields. Crossheads, bearings, and gas valves are important components of reciprocating compressors. They have the advantages of high operating accuracy and good replaceability. However, due to alternating loads, processing errors, and improper installation Affected by factors such as crossheads and sliding bearings, clearance failures caused by friction and wear of sliding bearings, and failures such as fracture and wear of air valves are prone to occur, resulting in reciprocating compressors not working normally, and even catastrophic accidents may occur. In addition, since the vibration signal of the reciprocating compressor has strong non-stationary and nonlinear characteristics, and the...

AI Technical Summary

Problems solved by technology

Commonly used methods such as empirical mode decomposition and local mean decomposition are prone to inherent mode aliasing problems and have certain limitations.

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