Rotation machinery fault diagnosis method based on singular spectrum decomposition

Abstract

The invention discloses a rotation machinery fault diagnosis method based on singular spectrum decomposition. The method comprises the following steps: 1) arranging a sensor near a key component of a rotation machinery, and collecting a measuring signal as a source signal; 2) carrying out decomposition on the source signal through singular spectrum decomposition to obtain a plurality of singular spectrum components, of which the instantaneous frequency has physical meaning; 3) according to characteristic energy factor maximum criterion, selecting a decomposed component having rich fault characteristic information as a main singular spectrum component; 4) carrying out Hilbert demodulation on the main singular spectrum component to obtain corresponding envelope spectrums; and 5) observing whether the fault characteristic frequency in the envelope spectrums has obvious peak, thereby realizing accurate judgment of fault type of the rotation machinery. The method is simple and practicable, and can realize more accurate fault diagnosis compared with other methods in the prior art.

Description

technical field [0001] The invention belongs to the technical field of mechanical engineering, and in particular relates to a fault diagnosis method for rotating machinery. Background technique [0002] Common components in industrial sites, such as bearings, gears, rotors, etc., are important components of rotating machinery. Due to long-term high-speed operation, alternating loads and other harsh working conditions, these key components are prone to local damage and damage. It evolves into a late failure, which greatly affects the performance of the entire rotating machinery transmission system. Therefore, effective fault detection for key components in rotating machinery has important practical significance. Due to the non-stationary characteristics of the vibration signal of rotating machinery in actual engineering, it is affected by the coupling effect of multiple transmission paths between components, and the vibration signal is doped with strong background noise and ...

AI Technical Summary

Problems solved by technology

Singular spectrum analysis is a non-parametric spectrum estimation method based on principal component analysis, including the selection of embedding dimensions, singular value decomposition and reconstruction of component sequences, etc. It can be effectively used for noise reduction processing of rotating machinery measurement signals, but It mainly relies on empirically selecting the embedding dimension length to construct the trajectory matrix, and performs singular value decomposition, feature reorganization and signal reconstruction based on the principal components of the trajectory matrix, which greatly affects its diagnostic results

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